An issue in Keras 3.7.0 allows attackers to write arbitrary files to the user's machine via downloading a crafted tar file through the get_file function.

Affected versions of the keras package are vulnerable to Deserialization of Untrusted Data due to the unconditional loading of external TensorFlow SavedModels during deserialization of .keras models in the TFSMLayer class. The flaw arises from the serialization of attacker-controlled file paths and the lack of validation in the from_config() method, which bypasses the safe_mode=True guarantee when a crafted .keras model is loaded. An attacker who supplies a malicious model can achieve arbitrary code execution during model inference under the victim's privileges.

A arbitrary code injection vulnerability in TensorFlow's Keras framework (<2.13) allows attackers to execute arbitrary code with the same permissions as the application using a model that allow arbitrary code irrespective of the application. See CVE-2024-3660.

Keras 2.6.0rc3 fixes a security issue for loading keras models via yaml, which could allow arbitrary code execution. See CVE-2021-37678.
https://github.com/keras-team/keras/commit/32febb6cdebe166fc70ad72f2da37607c13bb11a

Affected versions of the Keras package are vulnerable to Deserialization of Untrusted Data due to improper enforcement of safe mode protections in .keras model archives. The Model.load_model method can be bypassed even with safe_mode=True by crafting a malicious .keras archive containing a config.json file that invokes keras.config.enable_unsafe_deserialization() to disable safe mode before Lambda layers with arbitrary pickled code are processed. An attacker can exploit this by distributing a specially crafted .keras model that first disables safe mode protections through the configuration file and then executes arbitrary Python code via Lambda layers, achieving remote code execution despite the user's intention to load the model safely.

Affected versions of the Keras package are vulnerable to Information Disclosure and Server-Side Request Forgery (SSRF) due to insufficient restriction of the StringLookup.vocabulary path during model deserialization, even when safe_mode=True is used. The keras.models.load_model method will accept a crafted .keras archive that configures a StringLookup layer whose vocabulary points to arbitrary local or remote locations resolved via tf.io.gfile, causing file contents to be loaded into model state and retrievable via get_vocabulary, and enabling outbound fetches to attacker-chosen endpoints.

Pyyaml 5.3.1 includes a fix for CVE-2020-1747: A vulnerability was discovered in the PyYAML library in versions before 5.3.1, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. An attacker could use this flaw to execute arbitrary code on the system by abusing the python/object/new constructor.

PyYAML 5.1 through 5.1.2 has insufficient restrictions on the load and load_all functions because of a class deserialization issue, e.g., Popen is a class in the subprocess module. See CVE-2019-20477. NOTE: this issue exists because of an incomplete fix for CVE-2017-18342.

Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative values. 
NOTE: The vendor does not agree this is a vulnerability; the negative dimensions can only be created by an already privileged user (or internally).
https://github.com/numpy/numpy/issues/19000

Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service. 
NOTE: The vendor does not agree this is a vulnerability. In (very limited) circumstances a user may be able provoke the buffer overflow, the user is most likely already privileged to at least provoke denial of service by exhausting memory. Triggering this further requires the use of uncommon API (complicated structured dtypes), which is very unlikely to be available to an unprivileged user.
https://github.com/numpy/numpy/issues/18939

Numpy 1.22.0 includes a fix for CVE-2021-34141: An incomplete string comparison in the numpy.core component in NumPy before 1.22.0 allows attackers to trigger slightly incorrect copying by constructing specific string objects. 
NOTE: the vendor states that this reported code behavior is "completely harmless."
https://github.com/numpy/numpy/issues/18993

Numpy 1.22.2  includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. 
NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place.
NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.

Rasterio 1.3.8.post2 updates its bundled dependency 'libcurl' to v8.4.0 to include a security fix.
https://github.com/rasterio/rasterio-wheels/issues/112

Rasterio 1.3.8.post2 updates its bundled dependency 'libcurl' to v8.4.0 to include a security fix.

Rasterio 1.3.8.post1 updates its C dependency 'libwebp' to v1.3.2 to include a fix for a high-risk vulnerability.
https://github.com/rasterio/rasterio/discussions/2924

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'json-c' to include a security fix.
https://github.com/rasterio/rasterio-wheels/issues/81

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41214: In affected versions, the shape inference code for 'tf.ragged.cross' has an undefined behavior due to binding a reference to 'nullptr'. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vwhq-49r4-gj9v
https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35992: 'CHECK' fail in 'TensorListFromTensor'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9v8w-xmr4-wgxp

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41195: In affected versions, the implementation of 'tf.math.segment_*' operations results in a 'CHECK'-fail related abort (and denial of service) if a segment id in 'segment_ids' is large. This is similar to CVE-2021-29584 (and similar to other reported vulnerabilities in TensorFlow localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using 'AddDim'. However, if the number of elements in the tensor overflows an 'int64_t' value, 'AddDim' results in a 'CHECK' failure which provokes a 'std::abort'. Instead, code should use 'AddDimWithStatus'. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cq76-mxrc-vchh

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35999: 'CHECK' fail in 'Conv2DBackpropInput'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-37jf-mjv6-xfqw

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36018: 'CHECK' fail in 'RaggedTensorToVariant'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6cv-4fmf-66xf

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27775.

Tensorflow versions 2.5.3, 2.6.3 and 2.7.1 update its dependency 'icu' to v69.1 to include a security fix.

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26267: In affected versions, the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27778.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25658: Prior to versions 2.12.0 and 2.11.1, an out of bounds read is in GRUBlockCellGrad.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-68v3-g9cm-rmm6

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41907: When 'tf.raw_ops.ResizeNearestNeighborGrad' is given a large 'size' input, it overflows.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-368v-7v32-52fx

Affected versions of Tensorflow are vulnerable to Denial of Service via CHECK-failure (assertion failure) in constant folding. The output_prop tensor has a shape that is controlled by user input and this can result in triggering one of the CHECKs in the PartialTensorShape constructor. This is an instance of TFSA-2021-198 (CVE-2021-41197).

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29203: Integer overflow in 'SpaceToBatchND'.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29198:  Missing validation which causes denial of service via 'SparseTensorToCSRSparseMatrix'.

TensorFlow 2.8.4, 2.9.3, 2.10.1 and 2.11.0 include a fix for CVE-2022-35935: 'CHECK' failure in 'SobolSample' via missing validation.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-97p7-w86h-vcf9
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqvq-fvhr-v6hc

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25665: Prior to versions 2.12.0 and 2.11.1, when 'SparseSparseMaximum' is given invalid sparse tensors as inputs, it can give a null pointer error.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-558h-mq8x-7q9g

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 update its dependency "SQLite" to handle CVE-2020-9327.

The Grappler component of TensorFlow has a susceptibility to a denial-of-service through a CHECK-failure during constant folding. This issue arises from the output_prop tensor, which has a user-controlled shape and can trigger one of the PartialTensorShape constructor's CHECKs. This flaw has been designated as TFSA-2021-198.

The problem has been addressed and rectified in the GitHub commit be7b286d40bc68cb0b56f702186cc4837d508058, which will be incorporated in TensorFlow 2.8.0. This fix will also be applied to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 versions as they too are within the affected and presently supported range.

For comprehensive information regarding our security model, how to get in touch with us for any queries or concerns, please refer to our security guide.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29205: Segfault due to missing support for quantized types.

Impact: A recurring instance of CVE-2022-35935 has been observed and addressed. In this case, `SobolSample` is prone to denial of service due to assumed scalar inputs. You can replicate this using the following code in Python:

```python 
import tensorflow as tf 
tf.raw_ops.SobolSample(dim=tf.constant([1,0]), num_results=tf.constant([1]), skip=tf.constant([1])) 
```

Patches: Corrective measures have been taken and the issue has been patched via GitHub commits c65c67f88ad770662e8f191269a907bf2b94b1bf and 02400ea266bd811fc016a848445de1bbff3a23a0. These fixes will be integrated in the forthcoming TensorFlow 2.11 release and will also be added to TensorFlow 2.10.1, 2.9.3, and 2.8.4 as they fall within the supported range. Furthermore, the initial commit will be incorporated into TensorFlow 2.7.4.

For more information: You can refer to the TensorFlow's security guide for comprehensive insights into the security model and for details on how to contact them for queries or issues.

Attribution: This vulnerability was reported by Kang Hong Jin from Singapore Management University, Neophytos Christou from Secure Systems Labs at Brown University, Liu Liyuan from the Information System & Security and Countermeasures Experiments Center at Beijing Institute of Technology, and Pattarakrit Rattankul.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23569: Multiple operations in TensorFlow can be used to trigger a denial of service via 'CHECK'-fails (i.e., assertion failures). This is similar to CVE-2021-41197 and has a similar fix. It is possible that other similar instances exist.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qj5r-f9mv-rffh

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25676: When running versions prior to 2.12.0 and 2.11.1 with XLA, 'tf.raw_ops.ParallelConcat' segfaults with a nullptr dereference when given a parameter 'shape' with rank that is not greater than zero.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6wfh-89q8-44jq

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41200: In affected versions, if 'tf.summary.create_file_writer' is called with non-scalar arguments, code crashes due to a 'CHECK'-fail. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh8h-7j2j-qv4f

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41900: The security vulnerability results in FractionalMax(AVG)Pool with illegal pooling_ratio. Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or remote code execution.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvwp-h6jv-7472

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29197: Missing validation which causes denial of service via 'UnsortedSegmentJoin'.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29213: Crashes stemming from incomplete validation in signal ops.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35937: OOB read in 'Gather_nd' op in TF Lite.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pxrw-j2fv-hx3h

Tensorflow 1.15 includes a fix for CVE-2019-16778: In TensorFlow before 1.15, a heap buffer overflow in UnsortedSegmentSum can be produced when the Index template argument is int32. In this case data_size and num_segments fields are truncated from int64 to int32 and can produce negative numbers, resulting in accessing out of bounds heap memory. This is unlikely to be exploitable and was detected and fixed internally in TensorFlow 1.15 and 2.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-844w-j86r-4x2j
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2019-002.md
https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892

TensorFlow updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38546.

Tensorflow 1.15.0rc0 includes a fix for a potential security vulnerability where decoding variant tensors from proto could result in heap out of bounds memory access.
https://github.com/tensorflow/tensorflow/issues/37701

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 update its dependency "Apache Spark" to handle CVE-2018-11770.

The effect of CVE-2022-35991 was seen once more, where TensorListScatter and TensorListScatterV2 could potentially crash due to non scalar inputs in the element_shape parameter while in eager mode. This issue has been identified and resolved. The issue was identified when the following Python code was executed:

```python 
import tensorflow as tf 
arg_0=tf.random.uniform(shape=(2, 2, 2), dtype=tf.float16, maxval=None) 
arg_1=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536) 
arg_2=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536) 
arg_3='' 
tf.raw_ops.TensorListScatter(tensor=arg_0, indices=arg_1,  element_shape=arg_2, name=arg_3) 
```

A patch to resolve this issue is available in the GitHub commit bf9932fc907aff0e9e8cccf769e8b00d30fd81a1. This fix will be part of TensorFlow 2.11. Additionally, the commitment will be selected for TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these versions are also known to be affected and still under supported range.

For further details, please refer to TensorFlow's security guide. If there is any issue or question, contact us please.

The person who brought this vulnerability to our attention is Pattarakrit Rattankul.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35934: 'CHECK' failure in tf.reshape via overflows.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f4w6-h4f5-wx45

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25660: Prior to versions 2.12.0 and 2.11.1, when the parameter 'summarize' of 'tf.raw_ops.Print' is zero, the new method 'SummarizeArray<bool>' will reference to a nullptr, leading to a seg fault.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qjqc-vqcf-5qvj

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15207: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses 'ResolveAxis' to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the 'DCHECK' does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q4qf-3fc6-8x34

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25663: Prior to versions 2.12.0 and 2.11.1, when 'ctx->step_containter()' is a null ptr, the Lookup function will be executed with a null pointer.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-64jg-wjww-7c5w

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29212: Core dump when loading TFLite models with quantization.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36011: Null dereference on MLIR on empty function attributes.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fv43-93gv-vm8f

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27776.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35940: Int overflow in 'RaggedRangeOp'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x989-q2pq-4q5x

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15204: In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling "tf.raw_ops.GetSessionHandle" or "tf.raw_ops.GetSessionHandleV2" results in a null pointer dereference In linked snippet, in eager mode, "ctx->session_state()" returns "nullptr". Since code immediately dereferences this, we get a segmentation fault. The issue was patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1

TensorFlow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41225: In affected versions, TensorFlow's Grappler optimizer has a use of unitialized variable. If the 'train_nodes' vector (obtained from the saved model that gets optimized) does not contain a 'Dequeue' node, then 'dequeue_node' is left unitialized. The fix is also included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw
https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21736: The implementation of 'SparseTensorSliceDataset' has an undefined behavior: under certain conditions, it can be made to dereference a 'nullptr' value. The 3 input arguments to 'SparseTensorSliceDataset' represent a sparse tensor. However, there are some preconditions that these arguments must satisfy, but these are not validated in the implementation.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pfjj-m3jj-9jc9

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21726: The implementation of 'Dequantize' does not fully validate the value of 'axis' and can result in heap OOB accesses. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26270: In affected versions, running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25667: Prior to versions 2.12.0 and 2.11.1, integer overflow occurs when '2^31 <= num_frames * height * width * channels < 2^32', for example Full HD screencast of at least 346 frames.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqm2-gh8w-gr68

Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a `SavedModel` file (fixing the first one would trigger the same dereference in the second place). First, during constant folding, the `GraphDef` might not have the required nodes for the binary operation. If a node is missing, the correposning `mul_*child` would be null, and the dereference in the subsequent line would be incorrect. We have a similar issue during `IsIdentityConsumingSwitch`. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41894: The reference kernel of the 'CONV_3D_TRANSPOSE' TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of 'data_ptr += num_channels;' it should be 'data_ptr += output_num_channels;' as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6q3-vv32-2cq5

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36004: 'CHECK' fail in 'tf.random.gamma'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv8m-8x97-937q

Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Affected versions of Tensorflow allow tensors to have a large number of dimensions and each dimension can be as large as desired. However, the total number of elements in a tensor must fit within an 'int64_t'. If an overflow occurs, 'MultiplyWithoutOverflow' would return a negative result. In the majority of TensorFlow codebase this then results in a 'CHECK'-failure. Newer constructs exist which return a 'Status' instead of crashing the binary. This is a similar issue to CVE-2021-29584.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23580: During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-627q-g293-49q7

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 update its dependency "SQLite3" to handle CVE-2019-19880.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27780.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36012: Assertion fail on MLIR empty edge names.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jvhc-5hhr-w3v5

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35959: 'CHECK' failures in 'AvgPool3DGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wxjj-cgcx-r3vq

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-30115.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21727: The implementation of shape inference for 'Dequantize' is vulnerable to an integer overflow weakness. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes 'axis + 1', an attacker can trigger an integer overflow.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c6fh-56w7-fvjw

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29194: Missing validation which causes denial of service via 'DeleteSessionTensor'.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29196: Missing validation which causes denial of service via 'Conv3DBackpropFilterV2'.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41215: In affected versions, the shape inference code for 'DeserializeSparse' can trigger a null pointer dereference. This is because the shape inference function assumes that the 'serialize_sparse' tensor is a tensor with positive rank (and having '3' as the last dimension). The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x3v8-c8qx-3j3r
https://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23558: An attacker can craft a TFLite model that would cause an integer overflow in 'TfLiteIntArrayCreate'. The 'TfLiteIntArrayGetSizeInBytes' returns an 'int' instead of a 'size_t'. An attacker can control model inputs such that 'computed_size' overflows the size of 'int' datatype.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9gwq-6cwj-47h3

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21741: An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-428x-9xc2-m8mj

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "libjpeg-turbo" to handle CVE-2019-13960.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23582: A malicious user can cause a denial of service by altering a 'SavedModel' such that 'TensorByteSize' would trigger 'CHECK' failures. 'TensorShape' constructor throws a 'CHECK'-fail if shape is partial or has a number of elements that would overflow the size of an 'int'. The 'PartialTensorShape' constructor instead does not cause a 'CHECK'-abort if the shape is partial, which is exactly what this function needs to be able to return '-1'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4j82-5ccr-4r8v

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35997: 'CHECK' fail in 'tf.sparse.cross'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p7hr-f446-x6qf

TensorFlow 2.4.0 includes a fix for CVE-2020-15266: In Tensorflow before version 2.4.0, when the 'boxes' argument of 'tf.image.crop_and_resize' has a very large value, the CPU kernel implementation receives it as a C++ 'nan' floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault.
https://github.com/tensorflow/tensorflow/issues/42129
https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41209: In affected versions, the implementations for convolution operators trigger a division by 0 if passed empty filter tensor arguments. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hpv-v2rx-c5g6
https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'zlib' to v1.2.12 to handle CVE-2018-25032.

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "libjpeg-turbo" to handle CVE-2018-20330.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41885: When 'tf.raw_ops.FusedResizeAndPadConv2D' is given a large tensor shape, it overflows.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-762h-vpvw-3rcx

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41219: In affected versions, the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to 'nullptr'. This occurs whenever the dimensions of 'a' or 'b' are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, it should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x
https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41910: The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-frqp-wp83-qggv

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41207: In affected versions, the implementation of 'ParallelConcat' misses some input validation and can produce a division by 0. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7v94-64hj-m82h
https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235

Tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateOutputSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements. We can have a large enough number of dimensions in `output_shape.dim()` or just a small number of dimensions being large enough to cause an overflow in the multiplication. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29209: Type confusion leading to 'CHECK'-failure based denial of service.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22923.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41204: In affected versions, during TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-786j-5qwq-r36x
https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35964: Segfault in 'BlockLSTMGradV2'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f7r5-q7cx-h668

Tensorflow is an Open Source Machine Learning Framework. The implementation of `AssignOp` can result in copying uninitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Some versions of tensorflow are vulnerable to an out-of-bounds read issue, where decoding variant tensors from proto could lead to unauthorized heap memory access. The exploit maturity for this vulnerability is currently unproven.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27782.

TensorFlow 2.4.0 includes a fix for CVE-2020-15194: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1."

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41202: In affected versions, while calculating the size of the output within the 'tf.range' kernel, there is a conditional statement of type 'int64 = condition ? int64 : double'. Due to C++ implicit conversion rules, both branches of the condition will be cast to 'double' and the result would be truncated before the assignment. This result in overflows. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xrqm-fpgr-6hhx

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23562: The implementation of 'Range' suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qx3f-p745-w4hr

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "Apache Spark" to handle CVE-2018-17190.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41898: If 'SparseFillEmptyRowsGrad' is given empty inputs, TensorFlow will crash.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hq7g-wwwp-q46h

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22922.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29202: Denial of service in 'tf.ragged.constant' due to lack of validation.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35979: Segfault in 'QuantizedRelu' and 'QuantizedRelu6'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v7vw-577f-vp8x

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13435.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29193: missing validation which causes 'TensorSummaryV2' to crash.

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26268: In affected versions, the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25675: When running versions prior to 2.12.0 and 2.11.1 with XLA, 'tf.raw_ops.Bincount' segfaults when given a parameter 'weights' that is neither the same shape as parameter 'arr' nor a length-0 tensor.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7x4v-9gxg-9hwj

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25672: The function 'tf.raw_ops.LookupTableImportV2' cannot handle scalars in the 'values' parameter and gives an NPE. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29195: Missing validation which causes denial of service via 'StagePeek'.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35995: 'CHECK' fail in 'AudioSummaryV2'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g9h5-vr8m-x2h4

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41908: TensorFlow is an open source platform for machine learning. An input 'token' that is not a UTF-8 bytestring will trigger a 'CHECK' fail in 'tf.raw_ops.PyFunc'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv77-9g28-cwg3

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35965: Segfault in 'LowerBound' and 'UpperBound'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qxpx-j395-pw36

Tensorflow versions 1.15.2  and 2.0.1 updates its dependency "curl" to handle CVE-2019-5481.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15205: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'data_splits' argument of 'tf.raw_ops.StringNGrams' lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after 'ee ff' are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g7p5-5759-qv46

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41911: When printing a tensor, we get it's data as a 'const char*' array (since that's the underlying storage) and then we typecast it to the element type. However, conversions from 'char' to 'bool' are undefined if the 'char' is not '0' or '1', so sanitizers/fuzzers will crash.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pf36-r9c6-h97j

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that assertions in `function.cc` would be falsified and crash the Python interpreter. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 1.15.2 and 2.0.1 includes a fix for CVE-2020-5215: In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29199: Missing validation which causes denial of service via 'LoadAndRemapMatrix'.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41899: TensorFlow is an open source platform for machine learning. Inputs 'dense_features' or 'example_state_data' not of rank 2 will trigger a 'CHECK' fail in 'SdcaOptimizer'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-27rc-728f-x5w2

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-27579: Constructing a tflite model with a paramater 'filter_input_channel' of less than 1 gives a FPE.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5w96-866f-6rm8

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21730: The implementation of 'FractionalAvgPoolGrad' does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vjg4-v33c-ggc4

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23595: When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so 'flr->config_proto' is 'nullptr'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fpcp-9h7m-ffpx

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that Grappler optimizer would attempt to build a tensor using a reference `dtype`. This would result in a crash due to a `CHECK`-fail in the `Tensor` constructor as reference types are not allowed. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35982: Segfault in 'SparseBincount'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-397c-5g2j-qxpv

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41218: In affected versions, the shape inference code for 'AllToAll' can be made to execute a division by 0. This occurs whenever the 'split_count' argument is 0. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9crf-c6qr-r273
https://github.com/tensorflow/tensorflow/commit/a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41228: In affected versions, TensorFlow's 'saved_model_cli' tool is vulnerable to a code injection as it calls 'eval' on user supplied strings. This can be used by attackers to run arbitrary code on the plaform where the CLI tool runs. However, given that the tool is always run manually, the impact of this is not severe. The issue has been patched by adding a 'safe' flag which defaults to 'True' and an explicit warning for users.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3rcw-9p9x-582v
https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7

TensorFlow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0 includes a fix for CVE-2020-26266: In affected versions and under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36026: 'CHECK' fail in 'QuantizeAndDequantizeV3'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9cr2-8pwr-fhfq

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35998: 'CHECK' fail in 'EmptyTensorList'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhw4-wwr7-gjc5

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41895: If 'MirrorPadGrad' is given outsize input 'paddings', TensorFlow will give a heap OOB error.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gq2j-cr96-gvqx

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35988: 'CHECK' fail in 'tf.linalg.matrix_rank'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9vqj-64pv-w55c

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35983: 'CHECK' fail in 'Save' and 'SaveSlices'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6vp-8q9j-whx4

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41210: In affected versions, the shape inference functions for 'SparseCountSparseOutput' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m342-ff57-4jcc
https://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22926.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21729: The implementation of 'UnravelIndex' is vulnerable to a division by zero caused by an integer overflow bug.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-34f9-hjfq-rr8j

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26271: In affected versions, under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22924.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15206: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's "SavedModel" protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using "tensorflow-serving" or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d. However, this was not enough, as #41097 reported a different failure mode. The issue was finally patched in commit df095206f25471e864a8e63a0f1caef53a0e3a6

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13630.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29200: Missing validation which causes denial of service via 'LSTMBlockCell'.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36014: Null-dereference in 'mlir::tfg::TFOp::nameAttr'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7j3m-8g3c-9qqq

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35972: Segfault in 'QuantizedBiasAdd'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4pc4-m9mj-v2r9

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35963: 'CHECK' failures in 'FractionalAvgPoolGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-84jm-4cf3-9jfm

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36017: Segfault in 'Requantize'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wqmc-pm8c-2jhc

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41880: When the 'BaseCandidateSamplerOp' function receives a value in 'true_classes' larger than 'range_max', a heap oob read occurs.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8w5g-3wcv-9g2j

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35941: 'CHECK' failure in 'AvgPoolOp'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mgmh-g2v6-mqw5

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23563: In multiple places, TensorFlow uses 'tempfile.mktemp' to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in 'mktemp' and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness). In several instances, TensorFlow was supposed to actually create a temporary directory instead of a file. This logic bug is hidden away by the 'mktemp' function usage. It was replaced 'mktemp' with the safer 'mkstemp'/'mkdtemp' functions, according to the usage pattern.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wc4g-r73w-x8mm

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25670: Versions prior to 2.12.0 and 2.11.1 have a null point error in QuantizedMatMulWithBiasAndDequantize with MKL enabled.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rq-hwc3-x77w

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15211: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative "-1" value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the "-1" index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue was patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83). A potential workaround would be to add a custom "Verifier" to the model loading code to ensure that only operators which accept optional inputs use the "-1" special value and only for the tensors that they expect to be optional. Since this allow-list type approach is error-prone, it's advised upgrading to the patched code.

A vulnerability in TensorFlow's `GatherNd` function can trigger an out-of-bounds memory read or crash when inputs exceed output sizes. This issue is resolved in a GitHub commit, which will be included in an upcoming TensorFlow release. Additionally, the fix will be applied to several previous versions that are still under support. This vulnerability has no known workarounds, so updating to a patched version is recommended.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29216: Code injection in 'saved_model_cli'.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35993: 'CHECK' fail in 'SetSize'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wq6q-6m32-9rv9

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41897: If 'FractionMaxPoolGrad' is given outsize inputs 'row_pooling_sequence' and 'col_pooling_sequence', TensorFlow will crash.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2w8-jw48-fr7j

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23581: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'IsSimplifiableReshape' would trigger 'CHECK' failures.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq86-3f29-px2c

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35971: 'CHECK' fail in 'FakeQuantWithMinMaxVars'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9fpg-838v-wpv7

Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments, if the tensors have an invalid `dtype` and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 update its dependency "SQLite" to handle CVE-2020-11655.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23557: An attacker can craft a TFLite model that would trigger a division by zero in 'BiasAndClamp' implementation. There is no check that the 'bias_size' is non zero.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf2j-f278-xh4v

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36019: 'CHECK' fail in 'FakeQuantWithMinMaxVarsPerChannel'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9j4v-pp28-mxv7

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29211: Segfault when 'tf.histogram_fixed_width' is called with NaN values.

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "Apache Spark" to handle CVE-2019-10099.

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "SQLite" to handle CVE-2019-19645.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41890: If 'BCast::ToShape' is given input larger than an 'int32', it will crash, despite being supposed to handle up to an 'int64'. An example can be seen in 'tf.experimental.numpy.outer' by passing in large input to the input 'b'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h246-cgh4-7475

TensorFlow is an open source platform for machine learning. An input 'sparse_matrix' that is not a matrix with a shape with rank 0 will trigger a 'CHECK' fail in 'tf.raw_ops.SparseMatrixNNZ'. We have patched the issue in GitHub commit f856d02e5322821aad155dad9b3acab1e9f5d693. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41227: In affected versions, the 'ImmutableConst' operation in TensorFlow can be tricked into reading arbitrary memory contents. This is because the 'tstring' TensorFlow string class has a special case for memory mapped strings but the operation itself does not offer any support for this datatype. The fix is also included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8c8-67vp-6mx7
https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b
https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15209: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a "nullptr" buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with "nullptr". However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue was patched in commit 0b5662bc.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35973: Segfault in 'QuantizedMatMul'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9v

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15203: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the 'fill' argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a 'printf' call is constructed. This may result in segmentation fault.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35968: 'CHECK' fail in 'AvgPoolGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2475-53vw-vp25

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21739: The implementation of 'QuantizedMaxPool' has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3mw4-6rj6-74g5

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29192: missing validation which crashes 'QuantizeAndDequantizeV4Grad'.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25664: Prior to versions 2.12.0 and 2.11.1, there is a heap buffer overflow in TAvgPoolGrad.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hg6-5c2q-7rcr

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35939: OOB write in 'scatter_nd' op in TF Lite.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-ffjm-4qwc-7cmf

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23560: An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvf-hxvg-f67v

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35990: 'CHECK' fail in 'FakeQuantWithMinMaxVarsPerChannelGradient'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h7ff-cfc9-wmmh

Tensorflow versions 1.15.2 and 2.0.1 updates 'sqlite3' to handle CVE-2019-16168.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25671: There is out-of-bounds access due to mismatched integer type sizes.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j5w9-hmfh-4cr6

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41212: In affected versions, the shape inference code for 'tf.ragged.cross' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fr77-rrx3-cp7g
https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21731: The implementation of shape inference for 'ConcatV2' can be used to trigger a denial of service attack via a segfault caused by a type confusion. The 'axis' argument is translated into 'concat_dim' in the 'ConcatShapeHelper' helper function. Then, a value for 'min_rank' is computed based on 'concat_dim'. This is then used to validate that the 'values' tensor has at least the required rank. However, 'WithRankAtLeast' receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that 'min_rank' is a 32-bits value and the value of 'axis', the 'rank' argument is a negative value, so the error check is bypassed.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m4hf-j54p-p353

TensorFlow 2.14.1 updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38545.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21728: The implementation of shape inference for 'ReverseSequence' does not fully validate the value of 'batch_dim' and can result in a heap OOB read. There is a check to make sure the value of 'batch_dim' does not go over the rank of the input, but there is no check for negative values. Negative dimensions are allowed in some cases to mimic Python's negative indexing (i.e., indexing from the end of the array), however if the value is too negative then the implementation of 'Dim' would access elements before the start of an array.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gmv-pjp9-p8w8

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2 and 2.3.2 update its dependency "PCRE" to handle CVE-2020-14155.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36027: Segfault TFLite converter on per-channel quantized transposed convolutions.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-79h2-q768-fpxr

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29204: Missing validation which causes denial of service via 'Conv3DBackpropFilterV2'.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23564: When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a 'CHECK' assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rcj-c8pj-v3m3

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36005: 'CHECK' fail in 'FakeQuantWithMinMaxVarsGradient'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r26c-679w-mrjm

Tensorflow versions 2.5.3, 2.6.3 and 2.7.1 include a fix for CVE-2021-41208: In affected versions, the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing 'nullptr's or via 'CHECK'-failures) as well as abuse undefined behavior (binding references to 'nullptr's). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. TensorFlow's boosted trees APIs will be deprecated in subsequent releases.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-57wx-m983-2f88
https://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6gw-r52c-724r

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23567: The implementations of 'Sparse*Cwise*' ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or 'CHECK'-fails when building new 'TensorShape' objects (so, assert failures based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrx2-r989-2c43

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41886: When 'tf.raw_ops.ImageProjectiveTransformV2' is given a large output shape, it overflows.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-54pp-c6pp-7fpx

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36003: 'CHECK' fail in 'RandomPoissonV2'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cv2p-32v3-vhwq

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41198: In affected versions, if 'tf.tile' is called with a large input argument, then the TensorFlow process will crash due to a 'CHECK'-failure caused by an overflow. The number of elements in the output tensor is too much for the 'int64_t' type and the overflow is detected via a 'CHECK' statement. This aborts the process. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2p25-55c9-h58q

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15195: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of 'SparseFillEmptyRowsGrad' uses a double indexing pattern. It is possible for 'reverse_index_map(i)' to be an index outside of bounds of 'grad_values', thus resulting in a heap buffer overflow.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35985: 'CHECK' fail in 'LRNGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9942-r22v-78cp

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29191: Missing validation which causes denial of service via 'GetSessionTensor'.

Tensorflow is an Open Source Machine Learning Framework. When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling `png::CommonInitDecode(..., &decode)`, the `decode` value contains allocated buffers which can only be freed by calling `png::CommonFreeDecode(&decode)`. However, several error case in the function implementation invoke the `OP_REQUIRES` macro which immediately terminates the execution of the function, without allowing for the memory free to occur. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21738: The implementation of 'SparseCountSparseOutput' can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4qx-4fjv-hmw6

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25674: Versions prior to 2.12.0 and 2.11.1 have a null pointer error in RandomShuffle with XLA enabled.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf97-q72m-7579

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25801: Prior to versions 2.12.0 and 2.11.1, 'nn_ops.fractional_avg_pool_v2' and 'nn_ops.fractional_max_pool_v2' require the first and fourth elements of their parameter 'pooling_ratio' to be equal to 1.0, as pooling on batch and channel dimensions is not supported.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f49c-87jh-g47q

TensorFlow 2.4.0 includes a fix for CVE-2020-15265: In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateTensorSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35974: Segfault in 'QuantizeDownAndShrinkRange'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vgvh-2pf4-jr2x

Tensorflow is an Open Source Machine Learning Framework. If a graph node is invalid, TensorFlow can leak memory in the implementation of `ImmutableExecutorState::Initialize`. Here, we set `item->kernel` to `nullptr` but it is a simple `OpKernel*` pointer so the memory that was previously allocated to it would leak. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27781.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23565: An attacker can trigger denial of service via assertion failure by altering a 'SavedModel' on disk such that 'AttrDef's of some operation are duplicated.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4v5p-v5h9-6xjx

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25668: Attackers using Tensorflow prior to 2.12.0 or 2.11.1 can access heap memory which is not in the control of user, leading to a crash or remote code execution.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gw97-ff7c-9v96

Tensorflow versions 1.15.2 and 2.0.1 update its dependency "SQLite" to handle CVE-2019-19646.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21732: The implementation of 'ThreadPoolHandle' can be used to trigger a denial of service attack by allocating too much memory. This is because the 'num_threads' argument is only checked to not be negative, but there is no upper bound on its value.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c582-c96p-r5cq

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23579: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'SafeToRemoveIdentity' would trigger 'CHECK' failures.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5f2r-qp73-37mr

Tensorflow is an Open Source Machine Learning Framework. TensorFlow is vulnerable to a heap OOB write in `Grappler`. The `set_output` function writes to an array at the specified index. Hence, this gives a malicious user a write primitive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41213: In affected versions, the code behind 'tf.function' API can be made to deadlock when two 'tf.function' decorated Python functions are mutually recursive. This occurs due to using a non-reentrant 'Lock' Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive 'tf.function', although this is not a frequent scenario.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf
https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15208: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a "DCHECK" which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue was patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d

Affected versions of Tensorflow are vulnerable to Integer Overflow. array_ops.upper_bound' causes a segfault when not given a rank 2 tensor. The flaw was fixed in May 30, 2023, but the CVE was published in July 30, 2024. It was noticed unpublished by the Safety CLI Cyber Security team.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41223: In affected versions, the implementation of 'FusedBatchNorm' kernels is vulnerable to a heap OOB access. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f54p-f6jp-4rhr
https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41884: If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. 
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jq6x-99hj-q636

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41217: In affected versions, the process of building the control flow graph for a TensorFlow model is vulnerable to a null pointer exception when nodes that should be paired are not. This occurs because the code assumes that the first node in the pairing (e.g., an 'Enter' node) always exists when encountering the second node (e.g., an 'Exit' node). When this is not the case, 'parent' is 'nullptr' so dereferencing it causes a crash. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5crj-c72x-m7gq
https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35952: 'CHECK' failures in 'UnbatchGradOp'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h5vq-gw2c-pq47

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41891: If 'tf.raw_ops.TensorListConcat' is given 'element_shape=[]', it results segmentation fault which can be used to trigger a denial of service attack.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-66vq-54fq-6jvv

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15202: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'Shard' API in TensorFlow expects the last argument to be a function taking two 'int64' (i.e., 'long long') arguments. However, there are several places in TensorFlow where a lambda taking 'int' or 'int32' arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6fg-mjxg-hqq4

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25659: Prior to versions 2.12.0 and 2.11.1, if the parameter 'indices' for 'DynamicStitch' does not match the shape of the parameter 'data', it can trigger an stack OOB read.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-93vr-9q9m-pj8p

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41889: If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a 'nullptr', which is not caught. An example can be seen in 'tf.compat.v1.extract_volume_patches' by passing in quantized tensors as input 'ksizes'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xxcj-rhqg-m46g

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41896: If 'ThreadUnsafeUnigramCandidateSampler' is given input 'filterbank_channel_count' greater than the allowed max size, TensorFlow will crash.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rmg2-f698-wq35

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36001: 'CHECK' fail in 'DrawBoundingBoxes'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jqm7-m5q7-3hm5

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35986: Segfault in 'RaggedBincount'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wr9v-g9vf-c74v

Tensorflow is an Open Source Machine Learning Framework. There is a typo in TensorFlow's `SpecializeType` which results in heap OOB read/write. Due to a typo, `arg` is initialized to the `i`th mutable argument in a loop where the loop index is `j`. Hence it is possible to assign to `arg` from outside the vector of arguments. Since this is a mutable proto value, it allows both read and write to outside of bounds data. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29201: Missing validation which results in undefined behavior in 'QuantizedConv2D'.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36002: 'CHECK' fail in 'Unbatch'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mh3m-62v7-68xg

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23568: The implementation of 'AddManySparseToTensorsMap' is vulnerable to an integer overflow which results in a 'CHECK'-fail when building new 'TensorShape' objects (so, an assert failure based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6445-fm66-fvq2

TensorFlow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41226: In affected versions, the implementation of 'SparseBinCount' is vulnerable to a heap OOB access. This is because of missing validation between the elements of the 'values' argument and the shape of the sparse output. The fix is also included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-374m-jm66-3vj8
https://github.com/tensorflow/tensorflow/commit/f410212e373eb2aec4c9e60bf3702eba99a38aba

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35984: 'CHECK' fail in 'ParameterizedTruncatedNormal'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2xf-8hgm-hpw5

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29206: Missing validation which results in undefined behavior in 'SparseTensorDenseAdd'.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21733: The implementation of 'StringNGrams' can be used to trigger a denial of service attack by causing an out of memory condition after an integer overflow. There is missing a validation on 'pad_witdh' and that result in computing a negative value for 'ngram_width' which is later used to allocate parts of the output.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98j8-c9q4-r38g

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35981: 'CHECK' fail in 'FractionalMaxPoolGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vxv8-r8q2-63xw

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15190: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the "tf.raw_ops.Switch" operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is "nullptr", hence we are binding a reference to "nullptr". This is undefined behavior and reported as an error if compiling with "-fsanitize=null". In this case, this results in a segmentation fault The issue was patched in commit da8558533d925694483d2c136a9220d6d49d843c
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4g9f-63rx-5cw4

Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, TensorFlow can fail to specialize a type during shape inference. This case is covered by the 'DCHECK' function however, 'DCHECK' is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the 'ValueOrDie' line. This results in an assertion failure as 'ret' contains an error 'Status', not a value. In the second case we also get a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41196: In affected versions, the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m539-j985-hcr8

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a use after free behavior when decoding PNG images. After `png::CommonFreeDecode(&decode)` gets called, the values of `decode.width` and `decode.height` are in an unspecified state. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27774.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27779.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29207: Issues arising from undefined behavior stemming from users supplying invalid resource handles.

TensorFlow 2.8.4, 2.9.3, 2.10.1 and 2.11.0 include a fix for CVE-2022-35991: 'CHECK' fail in 'TensorListScatter' and 'TensorListScatterV2'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vm7x-4qhj-rrcq
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xf83-q765-xm6m

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a 'SavedModel' such that any binary op would trigger 'CHECK' failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the 'dtype' no longer matches the 'dtype' expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If 'Tin' and 'Tout' don't match the type of data in 'out' and 'input_*' tensors then 'flat<*>' would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a 'CHECK' crash, hence a denial of service. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41221: In affected versions, the shape inference code for the 'Cudnn*' operations can be tricked into accessing invalid memory via a heap buffer overflow. This occurs because the ranks of the 'input', 'input_h' and 'input_c' parameters are not validated, but code assumes they have certain values. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqv6-3phm-hcwx
https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41222: In affected versions, the implementation of 'SplitV' can trigger a segfault if an attacker supplies negative arguments. This occurs whenever 'size_splits' contains more than one value and at least one value is negative. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cpf4-wx82-gxp6
https://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41199: In affected versions, if 'tf.image.resize' is called with a large input argument then the TensorFlow process will crash due to a 'CHECK'-failure caused by an overflow. The number of elements in the output tensor is too much for the 'int64_t' type and the overflow is detected via a 'CHECK' statement. This aborts the process. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hx2-qx8j-qjqm

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21725: The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13871.

Tensorflow versions 2.5.3, 2.6.3 and 2.7.1 include a fix for CVE-2021-41206: In affected versions, several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or 'CHECK'-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. These issues were discovered internally via tooling while working on improving/testing GPU op determinism. As such, there aren't reproducers and there will be multiple fixes for these issues.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pgcq-h79j-2f69
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-43q8-3fv7-pr5x

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35966: Segfault in 'QuantizedAvgPool'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4w68-4x85-mjj9

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29208: Segfault and OOB write due to incomplete validation in 'EditDistance'.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22925.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41205: In affected versions, the shape inference functions for the 'QuantizeAndDequantizeV*' operations can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f
https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21737: The implementation of '*Bincount' operations allows malicious users to cause denial of service by passing in arguments which would trigger a 'CHECK'-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in 'CHECK' failures later when the output tensors get allocated.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2vv-v9cg-qhh7

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41224: In affected versions, the implementation of 'SparseFillEmptyRows' can be made to trigger a heap OOB access. This occurs whenever the size of 'indices' does not match the size of 'values'. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v
https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21740: The implementation of 'SparseCountSparseOutput' is vulnerable to a heap overflow.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-44qp-9wwf-734r

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35970: Segfault in 'QuantizedInstanceNorm'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g35r-369w-3fqp

Tensorflow is an Open Source Machine Learning Framework. The implementation of `GetInitOp` is vulnerable to a crash caused by dereferencing a null pointer. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25666: Prior to versions 2.12.0 and 2.11.1, there is a floating point exception in AudioSpectrogram. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f637-vh3r-vfh2

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23561: An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c78-vcq7-7vxq

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-22576.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35989: 'CHECK' fail in 'MaxPool'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j43h-pgmg-5hjq

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41888: When running on GPU, 'tf.image.generate_bounding_box_proposals' receives a 'scores' input that must be of rank 4 but is not checked.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6x99-gv2v-q76v

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21734: The implementation of 'MapStage' is vulnerable to a 'CHECK'-fail if the key tensor is not a scalar.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gcvh-66ff-4mwm

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35967: Segfault in 'QuantizedAdd'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6h3-348g-6h5x

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25661: In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the 'Convolution3DTranspose' function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a 'Convolution3DTranspose' call.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq

Tensorflow versions 1.15.2  and 2.0.1 updates its dependency "curl" to handle CVE-2019-5482.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35994: 'CHECK' fail in 'CollectiveGather'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fhfc-2q7x-929f

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21735: The implementation of 'FractionalMaxPool' can be made to crash a TensorFlow process via a division by 0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-87v6-crgm-2gfj

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36015: Integer overflow in math ops. 
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rh87-q4vg-m45j

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35969: 'CHECK' fail in 'Conv2DBackpropInput'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q2c3-jpmc-gfjx

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15210: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25669: Prior to versions 2.12.0 and 2.11.1, if the stride and window size are not positive for 'tf.raw_ops.AvgPoolGrad', it can give a floating point exception.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rcf8-g8jv-vg6p

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41893: If 'tf.raw_ops.TensorListResize' is given a nonscalar value for input 'size', it results 'CHECK' fail which can be used to trigger a denial of service attack.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-67pf-62xr-q35m

Tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 updates its dependency "Libjpeg-turbo" to handle CVE-2020-13790.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36016: 'CHECK'-fail in 'tensorflow::full_type::SubstituteFromAttrs'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g468-qj8g-vcjc

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25662: Versions prior to 2.12.0 and 2.11.1 are vulnerable to integer overflow in EditDistance.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7jvm-xxmr-v5cw

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35987: 'CHECK' fail in 'DenseBincount'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w62h-8xjm-fv49

Tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency "PCRE" to fix CVE-2019-20838.

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "SQLite3" to handle CVE-2019-19244.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41201: In affected versions, during execution, 'EinsumHelper::ParseEquation()' is supposed to set the flags in 'input_has_ellipsis' vector and '*output_has_ellipsis' boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to 'true' and never assigns 'false'. This results in unitialized variable access if callers assume that 'EinsumHelper::ParseEquation()' always sets these flags. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j86v-p27c-73fm
https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6

Tensorflow 2.9.3 and 2.10.1 include a fix for CVE-2022-41887: 'tf.keras.losses.poisson' receives a 'y_pred' and 'y_true' that are passed through 'functor::mul' in 'BinaryOp'. If the resulting dimensions overflow an 'int32', TensorFlow will crash due to a size mismatch during broadcast assignment.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fvv-46hw-vpg3

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 update its dependency "SQLite" to handle CVE-2020-11656.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36000: 'CHECK' fail in 'Eig'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqxc-pvf8-2w9v

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23591: The 'GraphDef' format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a 'GraphDef' containing a fragment such as the following can be consumed when loading a 'SavedModel'. This would result in a stack overflow during execution as resolving each 'NodeDef' means resolving the function itself and its nodes.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-247x-2f9f-5wp7

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41909: An input 'encoded' that is not a valid 'CompositeTensorVariant' tensor will trigger a segfault in 'tf.raw_ops.CompositeTensorVariantToComponents'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjx6-v474-2ch9

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35996: Floating point exception in 'Conv2D'. 
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35960: 'CHECK' failure in 'TensorListReserve' via missing validation.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v5xg-3q2c-c2r4

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25673: Versions prior to 2.12.0 and 2.11.1 have a Floating Point Exception in TensorListSplit with XLA. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13631.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23559: An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both 'embedding_size' and 'lookup_size' are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41216: In affected versions, the shape inference function for 'Transpose' is vulnerable to a heap buffer overflow. This occurs whenever 'perm' contains negative elements. The shape inference function does not validate that the indices in 'perm' are all valid. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3ff2-r28g-w7h9
https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14

Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a `DCHECK`. However, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the dereferencing of the null pointer, whereas in the second case it results in a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36013: Null-dereference in 'mlir::tfg::GraphDefImporter::ConvertNodeDef'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-828c-5j5q-vrjq

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41203: In affected versions, an attacker can trigger undefined behavior, integer overflows, segfaults and 'CHECK'-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7pxj-m4jf-r6h2

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "libjpeg-turbo" to handle CVE-2018-19664.

Affected versions of TensorFlow are susceptible to a Denial of Service (DoS) attack caused by an issue similar to CVE-2022-35991, occurring in TensorListScatter and TensorListScatterV2 when non-scalar inputs are used.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13434.

Tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency 'Junit4' to v4.13.1 to include a security fix.

Affected versions of Tensorflow are vulnerable to Denial of Service in the implementation of depthwise ops via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor. This is another instance of TFSA-2021-198 (CVE-2021-41197).

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41902: The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cg88-rpvp-cjv5

* Disputed * Scikit-learn 1.1.0rc1 includes a fix for CVE-2020-28975: svm_predict_values in svm.cpp in Libsvm v324, as used in scikit-learn and other products, allows attackers to cause a denial of service (segmentation fault) via a crafted model SVM (introduced via pickle, json, or any other model permanence standard) with a large value in the _n_support array. 
NOTE: the scikit-learn vendor's position is that the behavior can only occur if the library's API is violated by an application that changes a private attribute.

A sensitive data leakage vulnerability was identified in affected versions of scikit-learn TfidfVectorizer. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.

Scikit-learn 0.24.2 includes a fix for a ReDoS vulnerability.
https://github.com/scikit-learn/scikit-learn/issues/19522

An issue in Keras 3.7.0 allows attackers to write arbitrary files to the user's machine via downloading a crafted tar file through the get_file function.

Affected versions of the keras package are vulnerable to Deserialization of Untrusted Data due to the unconditional loading of external TensorFlow SavedModels during deserialization of .keras models in the TFSMLayer class. The flaw arises from the serialization of attacker-controlled file paths and the lack of validation in the from_config() method, which bypasses the safe_mode=True guarantee when a crafted .keras model is loaded. An attacker who supplies a malicious model can achieve arbitrary code execution during model inference under the victim's privileges.

A arbitrary code injection vulnerability in TensorFlow's Keras framework (<2.13) allows attackers to execute arbitrary code with the same permissions as the application using a model that allow arbitrary code irrespective of the application. See CVE-2024-3660.

Keras 2.6.0rc3 fixes a security issue for loading keras models via yaml, which could allow arbitrary code execution. See CVE-2021-37678.
https://github.com/keras-team/keras/commit/32febb6cdebe166fc70ad72f2da37607c13bb11a

Affected versions of the Keras package are vulnerable to Deserialization of Untrusted Data due to improper enforcement of safe mode protections in .keras model archives. The Model.load_model method can be bypassed even with safe_mode=True by crafting a malicious .keras archive containing a config.json file that invokes keras.config.enable_unsafe_deserialization() to disable safe mode before Lambda layers with arbitrary pickled code are processed. An attacker can exploit this by distributing a specially crafted .keras model that first disables safe mode protections through the configuration file and then executes arbitrary Python code via Lambda layers, achieving remote code execution despite the user's intention to load the model safely.

Affected versions of the Keras package are vulnerable to Information Disclosure and Server-Side Request Forgery (SSRF) due to insufficient restriction of the StringLookup.vocabulary path during model deserialization, even when safe_mode=True is used. The keras.models.load_model method will accept a crafted .keras archive that configures a StringLookup layer whose vocabulary points to arbitrary local or remote locations resolved via tf.io.gfile, causing file contents to be loaded into model state and retrievable via get_vocabulary, and enabling outbound fetches to attacker-chosen endpoints.

Pyyaml 5.3.1 includes a fix for CVE-2020-1747: A vulnerability was discovered in the PyYAML library in versions before 5.3.1, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. An attacker could use this flaw to execute arbitrary code on the system by abusing the python/object/new constructor.

PyYAML 5.1 through 5.1.2 has insufficient restrictions on the load and load_all functions because of a class deserialization issue, e.g., Popen is a class in the subprocess module. See CVE-2019-20477. NOTE: this issue exists because of an incomplete fix for CVE-2017-18342.

Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative values. 
NOTE: The vendor does not agree this is a vulnerability; the negative dimensions can only be created by an already privileged user (or internally).
https://github.com/numpy/numpy/issues/19000

Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service. 
NOTE: The vendor does not agree this is a vulnerability. In (very limited) circumstances a user may be able provoke the buffer overflow, the user is most likely already privileged to at least provoke denial of service by exhausting memory. Triggering this further requires the use of uncommon API (complicated structured dtypes), which is very unlikely to be available to an unprivileged user.
https://github.com/numpy/numpy/issues/18939

Numpy 1.22.0 includes a fix for CVE-2021-34141: An incomplete string comparison in the numpy.core component in NumPy before 1.22.0 allows attackers to trigger slightly incorrect copying by constructing specific string objects. 
NOTE: the vendor states that this reported code behavior is "completely harmless."
https://github.com/numpy/numpy/issues/18993

Numpy 1.22.2  includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. 
NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place.
NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.

Rasterio 1.3.8.post2 updates its bundled dependency 'libcurl' to v8.4.0 to include a security fix.
https://github.com/rasterio/rasterio-wheels/issues/112

Rasterio 1.3.8.post2 updates its bundled dependency 'libcurl' to v8.4.0 to include a security fix.

Rasterio 1.3.8.post1 updates its C dependency 'libwebp' to v1.3.2 to include a fix for a high-risk vulnerability.
https://github.com/rasterio/rasterio/discussions/2924

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'json-c' to include a security fix.
https://github.com/rasterio/rasterio-wheels/issues/81

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41214: In affected versions, the shape inference code for 'tf.ragged.cross' has an undefined behavior due to binding a reference to 'nullptr'. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vwhq-49r4-gj9v
https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35992: 'CHECK' fail in 'TensorListFromTensor'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9v8w-xmr4-wgxp

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41195: In affected versions, the implementation of 'tf.math.segment_*' operations results in a 'CHECK'-fail related abort (and denial of service) if a segment id in 'segment_ids' is large. This is similar to CVE-2021-29584 (and similar to other reported vulnerabilities in TensorFlow localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using 'AddDim'. However, if the number of elements in the tensor overflows an 'int64_t' value, 'AddDim' results in a 'CHECK' failure which provokes a 'std::abort'. Instead, code should use 'AddDimWithStatus'. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cq76-mxrc-vchh

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35999: 'CHECK' fail in 'Conv2DBackpropInput'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-37jf-mjv6-xfqw

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36018: 'CHECK' fail in 'RaggedTensorToVariant'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6cv-4fmf-66xf

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27775.

Tensorflow versions 2.5.3, 2.6.3 and 2.7.1 update its dependency 'icu' to v69.1 to include a security fix.

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26267: In affected versions, the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27778.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25658: Prior to versions 2.12.0 and 2.11.1, an out of bounds read is in GRUBlockCellGrad.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-68v3-g9cm-rmm6

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41907: When 'tf.raw_ops.ResizeNearestNeighborGrad' is given a large 'size' input, it overflows.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-368v-7v32-52fx

Affected versions of Tensorflow are vulnerable to Denial of Service via CHECK-failure (assertion failure) in constant folding. The output_prop tensor has a shape that is controlled by user input and this can result in triggering one of the CHECKs in the PartialTensorShape constructor. This is an instance of TFSA-2021-198 (CVE-2021-41197).

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29203: Integer overflow in 'SpaceToBatchND'.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29198:  Missing validation which causes denial of service via 'SparseTensorToCSRSparseMatrix'.

TensorFlow 2.8.4, 2.9.3, 2.10.1 and 2.11.0 include a fix for CVE-2022-35935: 'CHECK' failure in 'SobolSample' via missing validation.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-97p7-w86h-vcf9
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqvq-fvhr-v6hc

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25665: Prior to versions 2.12.0 and 2.11.1, when 'SparseSparseMaximum' is given invalid sparse tensors as inputs, it can give a null pointer error.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-558h-mq8x-7q9g

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 update its dependency "SQLite" to handle CVE-2020-9327.

The Grappler component of TensorFlow has a susceptibility to a denial-of-service through a CHECK-failure during constant folding. This issue arises from the output_prop tensor, which has a user-controlled shape and can trigger one of the PartialTensorShape constructor's CHECKs. This flaw has been designated as TFSA-2021-198.

The problem has been addressed and rectified in the GitHub commit be7b286d40bc68cb0b56f702186cc4837d508058, which will be incorporated in TensorFlow 2.8.0. This fix will also be applied to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 versions as they too are within the affected and presently supported range.

For comprehensive information regarding our security model, how to get in touch with us for any queries or concerns, please refer to our security guide.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29205: Segfault due to missing support for quantized types.

Impact: A recurring instance of CVE-2022-35935 has been observed and addressed. In this case, `SobolSample` is prone to denial of service due to assumed scalar inputs. You can replicate this using the following code in Python:

```python 
import tensorflow as tf 
tf.raw_ops.SobolSample(dim=tf.constant([1,0]), num_results=tf.constant([1]), skip=tf.constant([1])) 
```

Patches: Corrective measures have been taken and the issue has been patched via GitHub commits c65c67f88ad770662e8f191269a907bf2b94b1bf and 02400ea266bd811fc016a848445de1bbff3a23a0. These fixes will be integrated in the forthcoming TensorFlow 2.11 release and will also be added to TensorFlow 2.10.1, 2.9.3, and 2.8.4 as they fall within the supported range. Furthermore, the initial commit will be incorporated into TensorFlow 2.7.4.

For more information: You can refer to the TensorFlow's security guide for comprehensive insights into the security model and for details on how to contact them for queries or issues.

Attribution: This vulnerability was reported by Kang Hong Jin from Singapore Management University, Neophytos Christou from Secure Systems Labs at Brown University, Liu Liyuan from the Information System & Security and Countermeasures Experiments Center at Beijing Institute of Technology, and Pattarakrit Rattankul.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23569: Multiple operations in TensorFlow can be used to trigger a denial of service via 'CHECK'-fails (i.e., assertion failures). This is similar to CVE-2021-41197 and has a similar fix. It is possible that other similar instances exist.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qj5r-f9mv-rffh

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25676: When running versions prior to 2.12.0 and 2.11.1 with XLA, 'tf.raw_ops.ParallelConcat' segfaults with a nullptr dereference when given a parameter 'shape' with rank that is not greater than zero.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6wfh-89q8-44jq

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41200: In affected versions, if 'tf.summary.create_file_writer' is called with non-scalar arguments, code crashes due to a 'CHECK'-fail. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh8h-7j2j-qv4f

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41900: The security vulnerability results in FractionalMax(AVG)Pool with illegal pooling_ratio. Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or remote code execution.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvwp-h6jv-7472

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29197: Missing validation which causes denial of service via 'UnsortedSegmentJoin'.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29213: Crashes stemming from incomplete validation in signal ops.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35937: OOB read in 'Gather_nd' op in TF Lite.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pxrw-j2fv-hx3h

Tensorflow 1.15 includes a fix for CVE-2019-16778: In TensorFlow before 1.15, a heap buffer overflow in UnsortedSegmentSum can be produced when the Index template argument is int32. In this case data_size and num_segments fields are truncated from int64 to int32 and can produce negative numbers, resulting in accessing out of bounds heap memory. This is unlikely to be exploitable and was detected and fixed internally in TensorFlow 1.15 and 2.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-844w-j86r-4x2j
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2019-002.md
https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892

TensorFlow updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38546.

Tensorflow 1.15.0rc0 includes a fix for a potential security vulnerability where decoding variant tensors from proto could result in heap out of bounds memory access.
https://github.com/tensorflow/tensorflow/issues/37701

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 update its dependency "Apache Spark" to handle CVE-2018-11770.

The effect of CVE-2022-35991 was seen once more, where TensorListScatter and TensorListScatterV2 could potentially crash due to non scalar inputs in the element_shape parameter while in eager mode. This issue has been identified and resolved. The issue was identified when the following Python code was executed:

```python 
import tensorflow as tf 
arg_0=tf.random.uniform(shape=(2, 2, 2), dtype=tf.float16, maxval=None) 
arg_1=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536) 
arg_2=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536) 
arg_3='' 
tf.raw_ops.TensorListScatter(tensor=arg_0, indices=arg_1,  element_shape=arg_2, name=arg_3) 
```

A patch to resolve this issue is available in the GitHub commit bf9932fc907aff0e9e8cccf769e8b00d30fd81a1. This fix will be part of TensorFlow 2.11. Additionally, the commitment will be selected for TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these versions are also known to be affected and still under supported range.

For further details, please refer to TensorFlow's security guide. If there is any issue or question, contact us please.

The person who brought this vulnerability to our attention is Pattarakrit Rattankul.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35934: 'CHECK' failure in tf.reshape via overflows.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f4w6-h4f5-wx45

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25660: Prior to versions 2.12.0 and 2.11.1, when the parameter 'summarize' of 'tf.raw_ops.Print' is zero, the new method 'SummarizeArray<bool>' will reference to a nullptr, leading to a seg fault.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qjqc-vqcf-5qvj

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15207: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses 'ResolveAxis' to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the 'DCHECK' does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q4qf-3fc6-8x34

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25663: Prior to versions 2.12.0 and 2.11.1, when 'ctx->step_containter()' is a null ptr, the Lookup function will be executed with a null pointer.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-64jg-wjww-7c5w

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29212: Core dump when loading TFLite models with quantization.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36011: Null dereference on MLIR on empty function attributes.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fv43-93gv-vm8f

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27776.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35940: Int overflow in 'RaggedRangeOp'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x989-q2pq-4q5x

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15204: In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling "tf.raw_ops.GetSessionHandle" or "tf.raw_ops.GetSessionHandleV2" results in a null pointer dereference In linked snippet, in eager mode, "ctx->session_state()" returns "nullptr". Since code immediately dereferences this, we get a segmentation fault. The issue was patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1

TensorFlow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41225: In affected versions, TensorFlow's Grappler optimizer has a use of unitialized variable. If the 'train_nodes' vector (obtained from the saved model that gets optimized) does not contain a 'Dequeue' node, then 'dequeue_node' is left unitialized. The fix is also included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw
https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21736: The implementation of 'SparseTensorSliceDataset' has an undefined behavior: under certain conditions, it can be made to dereference a 'nullptr' value. The 3 input arguments to 'SparseTensorSliceDataset' represent a sparse tensor. However, there are some preconditions that these arguments must satisfy, but these are not validated in the implementation.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pfjj-m3jj-9jc9

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21726: The implementation of 'Dequantize' does not fully validate the value of 'axis' and can result in heap OOB accesses. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26270: In affected versions, running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25667: Prior to versions 2.12.0 and 2.11.1, integer overflow occurs when '2^31 <= num_frames * height * width * channels < 2^32', for example Full HD screencast of at least 346 frames.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqm2-gh8w-gr68

Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a `SavedModel` file (fixing the first one would trigger the same dereference in the second place). First, during constant folding, the `GraphDef` might not have the required nodes for the binary operation. If a node is missing, the correposning `mul_*child` would be null, and the dereference in the subsequent line would be incorrect. We have a similar issue during `IsIdentityConsumingSwitch`. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41894: The reference kernel of the 'CONV_3D_TRANSPOSE' TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of 'data_ptr += num_channels;' it should be 'data_ptr += output_num_channels;' as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6q3-vv32-2cq5

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36004: 'CHECK' fail in 'tf.random.gamma'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv8m-8x97-937q

Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Affected versions of Tensorflow allow tensors to have a large number of dimensions and each dimension can be as large as desired. However, the total number of elements in a tensor must fit within an 'int64_t'. If an overflow occurs, 'MultiplyWithoutOverflow' would return a negative result. In the majority of TensorFlow codebase this then results in a 'CHECK'-failure. Newer constructs exist which return a 'Status' instead of crashing the binary. This is a similar issue to CVE-2021-29584.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23580: During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-627q-g293-49q7

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 update its dependency "SQLite3" to handle CVE-2019-19880.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27780.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36012: Assertion fail on MLIR empty edge names.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jvhc-5hhr-w3v5

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35959: 'CHECK' failures in 'AvgPool3DGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wxjj-cgcx-r3vq

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-30115.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21727: The implementation of shape inference for 'Dequantize' is vulnerable to an integer overflow weakness. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes 'axis + 1', an attacker can trigger an integer overflow.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c6fh-56w7-fvjw

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29194: Missing validation which causes denial of service via 'DeleteSessionTensor'.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29196: Missing validation which causes denial of service via 'Conv3DBackpropFilterV2'.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41215: In affected versions, the shape inference code for 'DeserializeSparse' can trigger a null pointer dereference. This is because the shape inference function assumes that the 'serialize_sparse' tensor is a tensor with positive rank (and having '3' as the last dimension). The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x3v8-c8qx-3j3r
https://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23558: An attacker can craft a TFLite model that would cause an integer overflow in 'TfLiteIntArrayCreate'. The 'TfLiteIntArrayGetSizeInBytes' returns an 'int' instead of a 'size_t'. An attacker can control model inputs such that 'computed_size' overflows the size of 'int' datatype.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9gwq-6cwj-47h3

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21741: An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-428x-9xc2-m8mj

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "libjpeg-turbo" to handle CVE-2019-13960.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23582: A malicious user can cause a denial of service by altering a 'SavedModel' such that 'TensorByteSize' would trigger 'CHECK' failures. 'TensorShape' constructor throws a 'CHECK'-fail if shape is partial or has a number of elements that would overflow the size of an 'int'. The 'PartialTensorShape' constructor instead does not cause a 'CHECK'-abort if the shape is partial, which is exactly what this function needs to be able to return '-1'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4j82-5ccr-4r8v

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35997: 'CHECK' fail in 'tf.sparse.cross'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p7hr-f446-x6qf

TensorFlow 2.4.0 includes a fix for CVE-2020-15266: In Tensorflow before version 2.4.0, when the 'boxes' argument of 'tf.image.crop_and_resize' has a very large value, the CPU kernel implementation receives it as a C++ 'nan' floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault.
https://github.com/tensorflow/tensorflow/issues/42129
https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41209: In affected versions, the implementations for convolution operators trigger a division by 0 if passed empty filter tensor arguments. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hpv-v2rx-c5g6
https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'zlib' to v1.2.12 to handle CVE-2018-25032.

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "libjpeg-turbo" to handle CVE-2018-20330.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41885: When 'tf.raw_ops.FusedResizeAndPadConv2D' is given a large tensor shape, it overflows.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-762h-vpvw-3rcx

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41219: In affected versions, the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to 'nullptr'. This occurs whenever the dimensions of 'a' or 'b' are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, it should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x
https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41910: The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-frqp-wp83-qggv

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41207: In affected versions, the implementation of 'ParallelConcat' misses some input validation and can produce a division by 0. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7v94-64hj-m82h
https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235

Tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateOutputSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements. We can have a large enough number of dimensions in `output_shape.dim()` or just a small number of dimensions being large enough to cause an overflow in the multiplication. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29209: Type confusion leading to 'CHECK'-failure based denial of service.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22923.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41204: In affected versions, during TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-786j-5qwq-r36x
https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35964: Segfault in 'BlockLSTMGradV2'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f7r5-q7cx-h668

Tensorflow is an Open Source Machine Learning Framework. The implementation of `AssignOp` can result in copying uninitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Some versions of tensorflow are vulnerable to an out-of-bounds read issue, where decoding variant tensors from proto could lead to unauthorized heap memory access. The exploit maturity for this vulnerability is currently unproven.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27782.

TensorFlow 2.4.0 includes a fix for CVE-2020-15194: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1."

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41202: In affected versions, while calculating the size of the output within the 'tf.range' kernel, there is a conditional statement of type 'int64 = condition ? int64 : double'. Due to C++ implicit conversion rules, both branches of the condition will be cast to 'double' and the result would be truncated before the assignment. This result in overflows. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xrqm-fpgr-6hhx

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23562: The implementation of 'Range' suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qx3f-p745-w4hr

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "Apache Spark" to handle CVE-2018-17190.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41898: If 'SparseFillEmptyRowsGrad' is given empty inputs, TensorFlow will crash.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hq7g-wwwp-q46h

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22922.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29202: Denial of service in 'tf.ragged.constant' due to lack of validation.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35979: Segfault in 'QuantizedRelu' and 'QuantizedRelu6'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v7vw-577f-vp8x

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13435.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29193: missing validation which causes 'TensorSummaryV2' to crash.

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26268: In affected versions, the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25675: When running versions prior to 2.12.0 and 2.11.1 with XLA, 'tf.raw_ops.Bincount' segfaults when given a parameter 'weights' that is neither the same shape as parameter 'arr' nor a length-0 tensor.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7x4v-9gxg-9hwj

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25672: The function 'tf.raw_ops.LookupTableImportV2' cannot handle scalars in the 'values' parameter and gives an NPE. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29195: Missing validation which causes denial of service via 'StagePeek'.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35995: 'CHECK' fail in 'AudioSummaryV2'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g9h5-vr8m-x2h4

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41908: TensorFlow is an open source platform for machine learning. An input 'token' that is not a UTF-8 bytestring will trigger a 'CHECK' fail in 'tf.raw_ops.PyFunc'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv77-9g28-cwg3

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35965: Segfault in 'LowerBound' and 'UpperBound'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qxpx-j395-pw36

Tensorflow versions 1.15.2  and 2.0.1 updates its dependency "curl" to handle CVE-2019-5481.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15205: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'data_splits' argument of 'tf.raw_ops.StringNGrams' lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after 'ee ff' are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g7p5-5759-qv46

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41911: When printing a tensor, we get it's data as a 'const char*' array (since that's the underlying storage) and then we typecast it to the element type. However, conversions from 'char' to 'bool' are undefined if the 'char' is not '0' or '1', so sanitizers/fuzzers will crash.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pf36-r9c6-h97j

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that assertions in `function.cc` would be falsified and crash the Python interpreter. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 1.15.2 and 2.0.1 includes a fix for CVE-2020-5215: In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29199: Missing validation which causes denial of service via 'LoadAndRemapMatrix'.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41899: TensorFlow is an open source platform for machine learning. Inputs 'dense_features' or 'example_state_data' not of rank 2 will trigger a 'CHECK' fail in 'SdcaOptimizer'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-27rc-728f-x5w2

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-27579: Constructing a tflite model with a paramater 'filter_input_channel' of less than 1 gives a FPE.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5w96-866f-6rm8

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21730: The implementation of 'FractionalAvgPoolGrad' does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vjg4-v33c-ggc4

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23595: When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so 'flr->config_proto' is 'nullptr'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fpcp-9h7m-ffpx

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that Grappler optimizer would attempt to build a tensor using a reference `dtype`. This would result in a crash due to a `CHECK`-fail in the `Tensor` constructor as reference types are not allowed. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35982: Segfault in 'SparseBincount'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-397c-5g2j-qxpv

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41218: In affected versions, the shape inference code for 'AllToAll' can be made to execute a division by 0. This occurs whenever the 'split_count' argument is 0. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9crf-c6qr-r273
https://github.com/tensorflow/tensorflow/commit/a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41228: In affected versions, TensorFlow's 'saved_model_cli' tool is vulnerable to a code injection as it calls 'eval' on user supplied strings. This can be used by attackers to run arbitrary code on the plaform where the CLI tool runs. However, given that the tool is always run manually, the impact of this is not severe. The issue has been patched by adding a 'safe' flag which defaults to 'True' and an explicit warning for users.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3rcw-9p9x-582v
https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7

TensorFlow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0 includes a fix for CVE-2020-26266: In affected versions and under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36026: 'CHECK' fail in 'QuantizeAndDequantizeV3'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9cr2-8pwr-fhfq

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35998: 'CHECK' fail in 'EmptyTensorList'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhw4-wwr7-gjc5

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41895: If 'MirrorPadGrad' is given outsize input 'paddings', TensorFlow will give a heap OOB error.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gq2j-cr96-gvqx

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35988: 'CHECK' fail in 'tf.linalg.matrix_rank'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9vqj-64pv-w55c

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35983: 'CHECK' fail in 'Save' and 'SaveSlices'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6vp-8q9j-whx4

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41210: In affected versions, the shape inference functions for 'SparseCountSparseOutput' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m342-ff57-4jcc
https://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22926.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21729: The implementation of 'UnravelIndex' is vulnerable to a division by zero caused by an integer overflow bug.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-34f9-hjfq-rr8j

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26271: In affected versions, under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22924.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15206: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's "SavedModel" protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using "tensorflow-serving" or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d. However, this was not enough, as #41097 reported a different failure mode. The issue was finally patched in commit df095206f25471e864a8e63a0f1caef53a0e3a6

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13630.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29200: Missing validation which causes denial of service via 'LSTMBlockCell'.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36014: Null-dereference in 'mlir::tfg::TFOp::nameAttr'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7j3m-8g3c-9qqq

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35972: Segfault in 'QuantizedBiasAdd'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4pc4-m9mj-v2r9

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35963: 'CHECK' failures in 'FractionalAvgPoolGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-84jm-4cf3-9jfm

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36017: Segfault in 'Requantize'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wqmc-pm8c-2jhc

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41880: When the 'BaseCandidateSamplerOp' function receives a value in 'true_classes' larger than 'range_max', a heap oob read occurs.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8w5g-3wcv-9g2j

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35941: 'CHECK' failure in 'AvgPoolOp'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mgmh-g2v6-mqw5

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23563: In multiple places, TensorFlow uses 'tempfile.mktemp' to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in 'mktemp' and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness). In several instances, TensorFlow was supposed to actually create a temporary directory instead of a file. This logic bug is hidden away by the 'mktemp' function usage. It was replaced 'mktemp' with the safer 'mkstemp'/'mkdtemp' functions, according to the usage pattern.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wc4g-r73w-x8mm

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25670: Versions prior to 2.12.0 and 2.11.1 have a null point error in QuantizedMatMulWithBiasAndDequantize with MKL enabled.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rq-hwc3-x77w

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15211: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative "-1" value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the "-1" index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue was patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83). A potential workaround would be to add a custom "Verifier" to the model loading code to ensure that only operators which accept optional inputs use the "-1" special value and only for the tensors that they expect to be optional. Since this allow-list type approach is error-prone, it's advised upgrading to the patched code.

A vulnerability in TensorFlow's `GatherNd` function can trigger an out-of-bounds memory read or crash when inputs exceed output sizes. This issue is resolved in a GitHub commit, which will be included in an upcoming TensorFlow release. Additionally, the fix will be applied to several previous versions that are still under support. This vulnerability has no known workarounds, so updating to a patched version is recommended.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29216: Code injection in 'saved_model_cli'.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35993: 'CHECK' fail in 'SetSize'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wq6q-6m32-9rv9

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41897: If 'FractionMaxPoolGrad' is given outsize inputs 'row_pooling_sequence' and 'col_pooling_sequence', TensorFlow will crash.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2w8-jw48-fr7j

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23581: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'IsSimplifiableReshape' would trigger 'CHECK' failures.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq86-3f29-px2c

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35971: 'CHECK' fail in 'FakeQuantWithMinMaxVars'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9fpg-838v-wpv7

Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments, if the tensors have an invalid `dtype` and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 update its dependency "SQLite" to handle CVE-2020-11655.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23557: An attacker can craft a TFLite model that would trigger a division by zero in 'BiasAndClamp' implementation. There is no check that the 'bias_size' is non zero.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf2j-f278-xh4v

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36019: 'CHECK' fail in 'FakeQuantWithMinMaxVarsPerChannel'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9j4v-pp28-mxv7

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29211: Segfault when 'tf.histogram_fixed_width' is called with NaN values.

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "Apache Spark" to handle CVE-2019-10099.

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "SQLite" to handle CVE-2019-19645.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41890: If 'BCast::ToShape' is given input larger than an 'int32', it will crash, despite being supposed to handle up to an 'int64'. An example can be seen in 'tf.experimental.numpy.outer' by passing in large input to the input 'b'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h246-cgh4-7475

TensorFlow is an open source platform for machine learning. An input 'sparse_matrix' that is not a matrix with a shape with rank 0 will trigger a 'CHECK' fail in 'tf.raw_ops.SparseMatrixNNZ'. We have patched the issue in GitHub commit f856d02e5322821aad155dad9b3acab1e9f5d693. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41227: In affected versions, the 'ImmutableConst' operation in TensorFlow can be tricked into reading arbitrary memory contents. This is because the 'tstring' TensorFlow string class has a special case for memory mapped strings but the operation itself does not offer any support for this datatype. The fix is also included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8c8-67vp-6mx7
https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b
https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15209: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a "nullptr" buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with "nullptr". However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue was patched in commit 0b5662bc.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35973: Segfault in 'QuantizedMatMul'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9v

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15203: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the 'fill' argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a 'printf' call is constructed. This may result in segmentation fault.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35968: 'CHECK' fail in 'AvgPoolGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2475-53vw-vp25

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21739: The implementation of 'QuantizedMaxPool' has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3mw4-6rj6-74g5

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29192: missing validation which crashes 'QuantizeAndDequantizeV4Grad'.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25664: Prior to versions 2.12.0 and 2.11.1, there is a heap buffer overflow in TAvgPoolGrad.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hg6-5c2q-7rcr

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35939: OOB write in 'scatter_nd' op in TF Lite.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-ffjm-4qwc-7cmf

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23560: An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvf-hxvg-f67v

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35990: 'CHECK' fail in 'FakeQuantWithMinMaxVarsPerChannelGradient'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h7ff-cfc9-wmmh

Tensorflow versions 1.15.2 and 2.0.1 updates 'sqlite3' to handle CVE-2019-16168.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25671: There is out-of-bounds access due to mismatched integer type sizes.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j5w9-hmfh-4cr6

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41212: In affected versions, the shape inference code for 'tf.ragged.cross' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fr77-rrx3-cp7g
https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21731: The implementation of shape inference for 'ConcatV2' can be used to trigger a denial of service attack via a segfault caused by a type confusion. The 'axis' argument is translated into 'concat_dim' in the 'ConcatShapeHelper' helper function. Then, a value for 'min_rank' is computed based on 'concat_dim'. This is then used to validate that the 'values' tensor has at least the required rank. However, 'WithRankAtLeast' receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that 'min_rank' is a 32-bits value and the value of 'axis', the 'rank' argument is a negative value, so the error check is bypassed.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m4hf-j54p-p353

TensorFlow 2.14.1 updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38545.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21728: The implementation of shape inference for 'ReverseSequence' does not fully validate the value of 'batch_dim' and can result in a heap OOB read. There is a check to make sure the value of 'batch_dim' does not go over the rank of the input, but there is no check for negative values. Negative dimensions are allowed in some cases to mimic Python's negative indexing (i.e., indexing from the end of the array), however if the value is too negative then the implementation of 'Dim' would access elements before the start of an array.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gmv-pjp9-p8w8

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2 and 2.3.2 update its dependency "PCRE" to handle CVE-2020-14155.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36027: Segfault TFLite converter on per-channel quantized transposed convolutions.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-79h2-q768-fpxr

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29204: Missing validation which causes denial of service via 'Conv3DBackpropFilterV2'.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23564: When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a 'CHECK' assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rcj-c8pj-v3m3

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36005: 'CHECK' fail in 'FakeQuantWithMinMaxVarsGradient'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r26c-679w-mrjm

Tensorflow versions 2.5.3, 2.6.3 and 2.7.1 include a fix for CVE-2021-41208: In affected versions, the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing 'nullptr's or via 'CHECK'-failures) as well as abuse undefined behavior (binding references to 'nullptr's). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. TensorFlow's boosted trees APIs will be deprecated in subsequent releases.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-57wx-m983-2f88
https://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6gw-r52c-724r

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23567: The implementations of 'Sparse*Cwise*' ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or 'CHECK'-fails when building new 'TensorShape' objects (so, assert failures based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrx2-r989-2c43

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41886: When 'tf.raw_ops.ImageProjectiveTransformV2' is given a large output shape, it overflows.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-54pp-c6pp-7fpx

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36003: 'CHECK' fail in 'RandomPoissonV2'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cv2p-32v3-vhwq

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41198: In affected versions, if 'tf.tile' is called with a large input argument, then the TensorFlow process will crash due to a 'CHECK'-failure caused by an overflow. The number of elements in the output tensor is too much for the 'int64_t' type and the overflow is detected via a 'CHECK' statement. This aborts the process. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2p25-55c9-h58q

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15195: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of 'SparseFillEmptyRowsGrad' uses a double indexing pattern. It is possible for 'reverse_index_map(i)' to be an index outside of bounds of 'grad_values', thus resulting in a heap buffer overflow.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35985: 'CHECK' fail in 'LRNGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9942-r22v-78cp

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29191: Missing validation which causes denial of service via 'GetSessionTensor'.

Tensorflow is an Open Source Machine Learning Framework. When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling `png::CommonInitDecode(..., &decode)`, the `decode` value contains allocated buffers which can only be freed by calling `png::CommonFreeDecode(&decode)`. However, several error case in the function implementation invoke the `OP_REQUIRES` macro which immediately terminates the execution of the function, without allowing for the memory free to occur. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21738: The implementation of 'SparseCountSparseOutput' can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4qx-4fjv-hmw6

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25674: Versions prior to 2.12.0 and 2.11.1 have a null pointer error in RandomShuffle with XLA enabled.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf97-q72m-7579

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25801: Prior to versions 2.12.0 and 2.11.1, 'nn_ops.fractional_avg_pool_v2' and 'nn_ops.fractional_max_pool_v2' require the first and fourth elements of their parameter 'pooling_ratio' to be equal to 1.0, as pooling on batch and channel dimensions is not supported.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f49c-87jh-g47q

TensorFlow 2.4.0 includes a fix for CVE-2020-15265: In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateTensorSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35974: Segfault in 'QuantizeDownAndShrinkRange'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vgvh-2pf4-jr2x

Tensorflow is an Open Source Machine Learning Framework. If a graph node is invalid, TensorFlow can leak memory in the implementation of `ImmutableExecutorState::Initialize`. Here, we set `item->kernel` to `nullptr` but it is a simple `OpKernel*` pointer so the memory that was previously allocated to it would leak. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27781.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23565: An attacker can trigger denial of service via assertion failure by altering a 'SavedModel' on disk such that 'AttrDef's of some operation are duplicated.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4v5p-v5h9-6xjx

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25668: Attackers using Tensorflow prior to 2.12.0 or 2.11.1 can access heap memory which is not in the control of user, leading to a crash or remote code execution.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gw97-ff7c-9v96

Tensorflow versions 1.15.2 and 2.0.1 update its dependency "SQLite" to handle CVE-2019-19646.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21732: The implementation of 'ThreadPoolHandle' can be used to trigger a denial of service attack by allocating too much memory. This is because the 'num_threads' argument is only checked to not be negative, but there is no upper bound on its value.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c582-c96p-r5cq

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23579: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'SafeToRemoveIdentity' would trigger 'CHECK' failures.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5f2r-qp73-37mr

Tensorflow is an Open Source Machine Learning Framework. TensorFlow is vulnerable to a heap OOB write in `Grappler`. The `set_output` function writes to an array at the specified index. Hence, this gives a malicious user a write primitive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41213: In affected versions, the code behind 'tf.function' API can be made to deadlock when two 'tf.function' decorated Python functions are mutually recursive. This occurs due to using a non-reentrant 'Lock' Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive 'tf.function', although this is not a frequent scenario.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf
https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15208: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a "DCHECK" which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue was patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d

Affected versions of Tensorflow are vulnerable to Integer Overflow. array_ops.upper_bound' causes a segfault when not given a rank 2 tensor. The flaw was fixed in May 30, 2023, but the CVE was published in July 30, 2024. It was noticed unpublished by the Safety CLI Cyber Security team.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41223: In affected versions, the implementation of 'FusedBatchNorm' kernels is vulnerable to a heap OOB access. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f54p-f6jp-4rhr
https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41884: If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. 
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jq6x-99hj-q636

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41217: In affected versions, the process of building the control flow graph for a TensorFlow model is vulnerable to a null pointer exception when nodes that should be paired are not. This occurs because the code assumes that the first node in the pairing (e.g., an 'Enter' node) always exists when encountering the second node (e.g., an 'Exit' node). When this is not the case, 'parent' is 'nullptr' so dereferencing it causes a crash. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5crj-c72x-m7gq
https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35952: 'CHECK' failures in 'UnbatchGradOp'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h5vq-gw2c-pq47

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41891: If 'tf.raw_ops.TensorListConcat' is given 'element_shape=[]', it results segmentation fault which can be used to trigger a denial of service attack.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-66vq-54fq-6jvv

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15202: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'Shard' API in TensorFlow expects the last argument to be a function taking two 'int64' (i.e., 'long long') arguments. However, there are several places in TensorFlow where a lambda taking 'int' or 'int32' arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6fg-mjxg-hqq4

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25659: Prior to versions 2.12.0 and 2.11.1, if the parameter 'indices' for 'DynamicStitch' does not match the shape of the parameter 'data', it can trigger an stack OOB read.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-93vr-9q9m-pj8p

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41889: If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a 'nullptr', which is not caught. An example can be seen in 'tf.compat.v1.extract_volume_patches' by passing in quantized tensors as input 'ksizes'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xxcj-rhqg-m46g

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41896: If 'ThreadUnsafeUnigramCandidateSampler' is given input 'filterbank_channel_count' greater than the allowed max size, TensorFlow will crash.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rmg2-f698-wq35

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36001: 'CHECK' fail in 'DrawBoundingBoxes'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jqm7-m5q7-3hm5

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35986: Segfault in 'RaggedBincount'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wr9v-g9vf-c74v

Tensorflow is an Open Source Machine Learning Framework. There is a typo in TensorFlow's `SpecializeType` which results in heap OOB read/write. Due to a typo, `arg` is initialized to the `i`th mutable argument in a loop where the loop index is `j`. Hence it is possible to assign to `arg` from outside the vector of arguments. Since this is a mutable proto value, it allows both read and write to outside of bounds data. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29201: Missing validation which results in undefined behavior in 'QuantizedConv2D'.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36002: 'CHECK' fail in 'Unbatch'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mh3m-62v7-68xg

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23568: The implementation of 'AddManySparseToTensorsMap' is vulnerable to an integer overflow which results in a 'CHECK'-fail when building new 'TensorShape' objects (so, an assert failure based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6445-fm66-fvq2

TensorFlow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41226: In affected versions, the implementation of 'SparseBinCount' is vulnerable to a heap OOB access. This is because of missing validation between the elements of the 'values' argument and the shape of the sparse output. The fix is also included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-374m-jm66-3vj8
https://github.com/tensorflow/tensorflow/commit/f410212e373eb2aec4c9e60bf3702eba99a38aba

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35984: 'CHECK' fail in 'ParameterizedTruncatedNormal'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2xf-8hgm-hpw5

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29206: Missing validation which results in undefined behavior in 'SparseTensorDenseAdd'.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21733: The implementation of 'StringNGrams' can be used to trigger a denial of service attack by causing an out of memory condition after an integer overflow. There is missing a validation on 'pad_witdh' and that result in computing a negative value for 'ngram_width' which is later used to allocate parts of the output.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98j8-c9q4-r38g

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35981: 'CHECK' fail in 'FractionalMaxPoolGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vxv8-r8q2-63xw

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15190: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the "tf.raw_ops.Switch" operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is "nullptr", hence we are binding a reference to "nullptr". This is undefined behavior and reported as an error if compiling with "-fsanitize=null". In this case, this results in a segmentation fault The issue was patched in commit da8558533d925694483d2c136a9220d6d49d843c
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4g9f-63rx-5cw4

Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, TensorFlow can fail to specialize a type during shape inference. This case is covered by the 'DCHECK' function however, 'DCHECK' is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the 'ValueOrDie' line. This results in an assertion failure as 'ret' contains an error 'Status', not a value. In the second case we also get a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41196: In affected versions, the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m539-j985-hcr8

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a use after free behavior when decoding PNG images. After `png::CommonFreeDecode(&decode)` gets called, the values of `decode.width` and `decode.height` are in an unspecified state. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27774.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27779.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29207: Issues arising from undefined behavior stemming from users supplying invalid resource handles.

TensorFlow 2.8.4, 2.9.3, 2.10.1 and 2.11.0 include a fix for CVE-2022-35991: 'CHECK' fail in 'TensorListScatter' and 'TensorListScatterV2'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vm7x-4qhj-rrcq
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xf83-q765-xm6m

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a 'SavedModel' such that any binary op would trigger 'CHECK' failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the 'dtype' no longer matches the 'dtype' expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If 'Tin' and 'Tout' don't match the type of data in 'out' and 'input_*' tensors then 'flat<*>' would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a 'CHECK' crash, hence a denial of service. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41221: In affected versions, the shape inference code for the 'Cudnn*' operations can be tricked into accessing invalid memory via a heap buffer overflow. This occurs because the ranks of the 'input', 'input_h' and 'input_c' parameters are not validated, but code assumes they have certain values. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqv6-3phm-hcwx
https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41222: In affected versions, the implementation of 'SplitV' can trigger a segfault if an attacker supplies negative arguments. This occurs whenever 'size_splits' contains more than one value and at least one value is negative. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cpf4-wx82-gxp6
https://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41199: In affected versions, if 'tf.image.resize' is called with a large input argument then the TensorFlow process will crash due to a 'CHECK'-failure caused by an overflow. The number of elements in the output tensor is too much for the 'int64_t' type and the overflow is detected via a 'CHECK' statement. This aborts the process. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hx2-qx8j-qjqm

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21725: The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13871.

Tensorflow versions 2.5.3, 2.6.3 and 2.7.1 include a fix for CVE-2021-41206: In affected versions, several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or 'CHECK'-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. These issues were discovered internally via tooling while working on improving/testing GPU op determinism. As such, there aren't reproducers and there will be multiple fixes for these issues.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pgcq-h79j-2f69
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-43q8-3fv7-pr5x

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35966: Segfault in 'QuantizedAvgPool'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4w68-4x85-mjj9

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29208: Segfault and OOB write due to incomplete validation in 'EditDistance'.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22925.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41205: In affected versions, the shape inference functions for the 'QuantizeAndDequantizeV*' operations can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f
https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21737: The implementation of '*Bincount' operations allows malicious users to cause denial of service by passing in arguments which would trigger a 'CHECK'-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in 'CHECK' failures later when the output tensors get allocated.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2vv-v9cg-qhh7

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41224: In affected versions, the implementation of 'SparseFillEmptyRows' can be made to trigger a heap OOB access. This occurs whenever the size of 'indices' does not match the size of 'values'. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v
https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21740: The implementation of 'SparseCountSparseOutput' is vulnerable to a heap overflow.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-44qp-9wwf-734r

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35970: Segfault in 'QuantizedInstanceNorm'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g35r-369w-3fqp

Tensorflow is an Open Source Machine Learning Framework. The implementation of `GetInitOp` is vulnerable to a crash caused by dereferencing a null pointer. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25666: Prior to versions 2.12.0 and 2.11.1, there is a floating point exception in AudioSpectrogram. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f637-vh3r-vfh2

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23561: An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c78-vcq7-7vxq

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-22576.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35989: 'CHECK' fail in 'MaxPool'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j43h-pgmg-5hjq

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41888: When running on GPU, 'tf.image.generate_bounding_box_proposals' receives a 'scores' input that must be of rank 4 but is not checked.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6x99-gv2v-q76v

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21734: The implementation of 'MapStage' is vulnerable to a 'CHECK'-fail if the key tensor is not a scalar.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gcvh-66ff-4mwm

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35967: Segfault in 'QuantizedAdd'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6h3-348g-6h5x

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25661: In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the 'Convolution3DTranspose' function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a 'Convolution3DTranspose' call.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq

Tensorflow versions 1.15.2  and 2.0.1 updates its dependency "curl" to handle CVE-2019-5482.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35994: 'CHECK' fail in 'CollectiveGather'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fhfc-2q7x-929f

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21735: The implementation of 'FractionalMaxPool' can be made to crash a TensorFlow process via a division by 0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-87v6-crgm-2gfj

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36015: Integer overflow in math ops. 
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rh87-q4vg-m45j

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35969: 'CHECK' fail in 'Conv2DBackpropInput'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q2c3-jpmc-gfjx

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15210: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25669: Prior to versions 2.12.0 and 2.11.1, if the stride and window size are not positive for 'tf.raw_ops.AvgPoolGrad', it can give a floating point exception.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rcf8-g8jv-vg6p

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41893: If 'tf.raw_ops.TensorListResize' is given a nonscalar value for input 'size', it results 'CHECK' fail which can be used to trigger a denial of service attack.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-67pf-62xr-q35m

Tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 updates its dependency "Libjpeg-turbo" to handle CVE-2020-13790.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36016: 'CHECK'-fail in 'tensorflow::full_type::SubstituteFromAttrs'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g468-qj8g-vcjc

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25662: Versions prior to 2.12.0 and 2.11.1 are vulnerable to integer overflow in EditDistance.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7jvm-xxmr-v5cw

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35987: 'CHECK' fail in 'DenseBincount'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w62h-8xjm-fv49

Tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency "PCRE" to fix CVE-2019-20838.

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "SQLite3" to handle CVE-2019-19244.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41201: In affected versions, during execution, 'EinsumHelper::ParseEquation()' is supposed to set the flags in 'input_has_ellipsis' vector and '*output_has_ellipsis' boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to 'true' and never assigns 'false'. This results in unitialized variable access if callers assume that 'EinsumHelper::ParseEquation()' always sets these flags. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j86v-p27c-73fm
https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6

Tensorflow 2.9.3 and 2.10.1 include a fix for CVE-2022-41887: 'tf.keras.losses.poisson' receives a 'y_pred' and 'y_true' that are passed through 'functor::mul' in 'BinaryOp'. If the resulting dimensions overflow an 'int32', TensorFlow will crash due to a size mismatch during broadcast assignment.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fvv-46hw-vpg3

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 update its dependency "SQLite" to handle CVE-2020-11656.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36000: 'CHECK' fail in 'Eig'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqxc-pvf8-2w9v

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23591: The 'GraphDef' format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a 'GraphDef' containing a fragment such as the following can be consumed when loading a 'SavedModel'. This would result in a stack overflow during execution as resolving each 'NodeDef' means resolving the function itself and its nodes.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-247x-2f9f-5wp7

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41909: An input 'encoded' that is not a valid 'CompositeTensorVariant' tensor will trigger a segfault in 'tf.raw_ops.CompositeTensorVariantToComponents'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjx6-v474-2ch9

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35996: Floating point exception in 'Conv2D'. 
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35960: 'CHECK' failure in 'TensorListReserve' via missing validation.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v5xg-3q2c-c2r4

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25673: Versions prior to 2.12.0 and 2.11.1 have a Floating Point Exception in TensorListSplit with XLA. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13631.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23559: An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both 'embedding_size' and 'lookup_size' are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41216: In affected versions, the shape inference function for 'Transpose' is vulnerable to a heap buffer overflow. This occurs whenever 'perm' contains negative elements. The shape inference function does not validate that the indices in 'perm' are all valid. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3ff2-r28g-w7h9
https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14

Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a `DCHECK`. However, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the dereferencing of the null pointer, whereas in the second case it results in a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36013: Null-dereference in 'mlir::tfg::GraphDefImporter::ConvertNodeDef'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-828c-5j5q-vrjq

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41203: In affected versions, an attacker can trigger undefined behavior, integer overflows, segfaults and 'CHECK'-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7pxj-m4jf-r6h2

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "libjpeg-turbo" to handle CVE-2018-19664.

Affected versions of TensorFlow are susceptible to a Denial of Service (DoS) attack caused by an issue similar to CVE-2022-35991, occurring in TensorListScatter and TensorListScatterV2 when non-scalar inputs are used.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13434.

Tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency 'Junit4' to v4.13.1 to include a security fix.

Affected versions of Tensorflow are vulnerable to Denial of Service in the implementation of depthwise ops via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor. This is another instance of TFSA-2021-198 (CVE-2021-41197).

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41902: The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cg88-rpvp-cjv5

* Disputed * Scikit-learn 1.1.0rc1 includes a fix for CVE-2020-28975: svm_predict_values in svm.cpp in Libsvm v324, as used in scikit-learn and other products, allows attackers to cause a denial of service (segmentation fault) via a crafted model SVM (introduced via pickle, json, or any other model permanence standard) with a large value in the _n_support array. 
NOTE: the scikit-learn vendor's position is that the behavior can only occur if the library's API is violated by an application that changes a private attribute.

A sensitive data leakage vulnerability was identified in affected versions of scikit-learn TfidfVectorizer. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.

Scikit-learn 0.24.2 includes a fix for a ReDoS vulnerability.
https://github.com/scikit-learn/scikit-learn/issues/19522

An issue in Keras 3.7.0 allows attackers to write arbitrary files to the user's machine via downloading a crafted tar file through the get_file function.

Affected versions of the keras package are vulnerable to Deserialization of Untrusted Data due to the unconditional loading of external TensorFlow SavedModels during deserialization of .keras models in the TFSMLayer class. The flaw arises from the serialization of attacker-controlled file paths and the lack of validation in the from_config() method, which bypasses the safe_mode=True guarantee when a crafted .keras model is loaded. An attacker who supplies a malicious model can achieve arbitrary code execution during model inference under the victim's privileges.

A arbitrary code injection vulnerability in TensorFlow's Keras framework (<2.13) allows attackers to execute arbitrary code with the same permissions as the application using a model that allow arbitrary code irrespective of the application. See CVE-2024-3660.

Keras 2.6.0rc3 fixes a security issue for loading keras models via yaml, which could allow arbitrary code execution. See CVE-2021-37678.
https://github.com/keras-team/keras/commit/32febb6cdebe166fc70ad72f2da37607c13bb11a

Affected versions of the Keras package are vulnerable to Deserialization of Untrusted Data due to improper enforcement of safe mode protections in .keras model archives. The Model.load_model method can be bypassed even with safe_mode=True by crafting a malicious .keras archive containing a config.json file that invokes keras.config.enable_unsafe_deserialization() to disable safe mode before Lambda layers with arbitrary pickled code are processed. An attacker can exploit this by distributing a specially crafted .keras model that first disables safe mode protections through the configuration file and then executes arbitrary Python code via Lambda layers, achieving remote code execution despite the user's intention to load the model safely.

Affected versions of the Keras package are vulnerable to Information Disclosure and Server-Side Request Forgery (SSRF) due to insufficient restriction of the StringLookup.vocabulary path during model deserialization, even when safe_mode=True is used. The keras.models.load_model method will accept a crafted .keras archive that configures a StringLookup layer whose vocabulary points to arbitrary local or remote locations resolved via tf.io.gfile, causing file contents to be loaded into model state and retrievable via get_vocabulary, and enabling outbound fetches to attacker-chosen endpoints.

Pyyaml 5.3.1 includes a fix for CVE-2020-1747: A vulnerability was discovered in the PyYAML library in versions before 5.3.1, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. An attacker could use this flaw to execute arbitrary code on the system by abusing the python/object/new constructor.

PyYAML 5.1 through 5.1.2 has insufficient restrictions on the load and load_all functions because of a class deserialization issue, e.g., Popen is a class in the subprocess module. See CVE-2019-20477. NOTE: this issue exists because of an incomplete fix for CVE-2017-18342.

Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative values. 
NOTE: The vendor does not agree this is a vulnerability; the negative dimensions can only be created by an already privileged user (or internally).
https://github.com/numpy/numpy/issues/19000

Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service. 
NOTE: The vendor does not agree this is a vulnerability. In (very limited) circumstances a user may be able provoke the buffer overflow, the user is most likely already privileged to at least provoke denial of service by exhausting memory. Triggering this further requires the use of uncommon API (complicated structured dtypes), which is very unlikely to be available to an unprivileged user.
https://github.com/numpy/numpy/issues/18939

Numpy 1.22.0 includes a fix for CVE-2021-34141: An incomplete string comparison in the numpy.core component in NumPy before 1.22.0 allows attackers to trigger slightly incorrect copying by constructing specific string objects. 
NOTE: the vendor states that this reported code behavior is "completely harmless."
https://github.com/numpy/numpy/issues/18993

Numpy 1.22.2  includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. 
NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place.
NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.

Rasterio 1.3.8.post2 updates its bundled dependency 'libcurl' to v8.4.0 to include a security fix.
https://github.com/rasterio/rasterio-wheels/issues/112

Rasterio 1.3.8.post2 updates its bundled dependency 'libcurl' to v8.4.0 to include a security fix.

Rasterio 1.3.8.post1 updates its C dependency 'libwebp' to v1.3.2 to include a fix for a high-risk vulnerability.
https://github.com/rasterio/rasterio/discussions/2924

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'json-c' to include a security fix.
https://github.com/rasterio/rasterio-wheels/issues/81

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41214: In affected versions, the shape inference code for 'tf.ragged.cross' has an undefined behavior due to binding a reference to 'nullptr'. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vwhq-49r4-gj9v
https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35992: 'CHECK' fail in 'TensorListFromTensor'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9v8w-xmr4-wgxp

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41195: In affected versions, the implementation of 'tf.math.segment_*' operations results in a 'CHECK'-fail related abort (and denial of service) if a segment id in 'segment_ids' is large. This is similar to CVE-2021-29584 (and similar to other reported vulnerabilities in TensorFlow localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using 'AddDim'. However, if the number of elements in the tensor overflows an 'int64_t' value, 'AddDim' results in a 'CHECK' failure which provokes a 'std::abort'. Instead, code should use 'AddDimWithStatus'. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cq76-mxrc-vchh

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35999: 'CHECK' fail in 'Conv2DBackpropInput'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-37jf-mjv6-xfqw

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36018: 'CHECK' fail in 'RaggedTensorToVariant'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6cv-4fmf-66xf

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27775.

Tensorflow versions 2.5.3, 2.6.3 and 2.7.1 update its dependency 'icu' to v69.1 to include a security fix.

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26267: In affected versions, the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27778.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25658: Prior to versions 2.12.0 and 2.11.1, an out of bounds read is in GRUBlockCellGrad.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-68v3-g9cm-rmm6

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41907: When 'tf.raw_ops.ResizeNearestNeighborGrad' is given a large 'size' input, it overflows.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-368v-7v32-52fx

Affected versions of Tensorflow are vulnerable to Denial of Service via CHECK-failure (assertion failure) in constant folding. The output_prop tensor has a shape that is controlled by user input and this can result in triggering one of the CHECKs in the PartialTensorShape constructor. This is an instance of TFSA-2021-198 (CVE-2021-41197).

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29203: Integer overflow in 'SpaceToBatchND'.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29198:  Missing validation which causes denial of service via 'SparseTensorToCSRSparseMatrix'.

TensorFlow 2.8.4, 2.9.3, 2.10.1 and 2.11.0 include a fix for CVE-2022-35935: 'CHECK' failure in 'SobolSample' via missing validation.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-97p7-w86h-vcf9
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqvq-fvhr-v6hc

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25665: Prior to versions 2.12.0 and 2.11.1, when 'SparseSparseMaximum' is given invalid sparse tensors as inputs, it can give a null pointer error.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-558h-mq8x-7q9g

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 update its dependency "SQLite" to handle CVE-2020-9327.

The Grappler component of TensorFlow has a susceptibility to a denial-of-service through a CHECK-failure during constant folding. This issue arises from the output_prop tensor, which has a user-controlled shape and can trigger one of the PartialTensorShape constructor's CHECKs. This flaw has been designated as TFSA-2021-198.

The problem has been addressed and rectified in the GitHub commit be7b286d40bc68cb0b56f702186cc4837d508058, which will be incorporated in TensorFlow 2.8.0. This fix will also be applied to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3 versions as they too are within the affected and presently supported range.

For comprehensive information regarding our security model, how to get in touch with us for any queries or concerns, please refer to our security guide.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29205: Segfault due to missing support for quantized types.

Impact: A recurring instance of CVE-2022-35935 has been observed and addressed. In this case, `SobolSample` is prone to denial of service due to assumed scalar inputs. You can replicate this using the following code in Python:

```python 
import tensorflow as tf 
tf.raw_ops.SobolSample(dim=tf.constant([1,0]), num_results=tf.constant([1]), skip=tf.constant([1])) 
```

Patches: Corrective measures have been taken and the issue has been patched via GitHub commits c65c67f88ad770662e8f191269a907bf2b94b1bf and 02400ea266bd811fc016a848445de1bbff3a23a0. These fixes will be integrated in the forthcoming TensorFlow 2.11 release and will also be added to TensorFlow 2.10.1, 2.9.3, and 2.8.4 as they fall within the supported range. Furthermore, the initial commit will be incorporated into TensorFlow 2.7.4.

For more information: You can refer to the TensorFlow's security guide for comprehensive insights into the security model and for details on how to contact them for queries or issues.

Attribution: This vulnerability was reported by Kang Hong Jin from Singapore Management University, Neophytos Christou from Secure Systems Labs at Brown University, Liu Liyuan from the Information System & Security and Countermeasures Experiments Center at Beijing Institute of Technology, and Pattarakrit Rattankul.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23569: Multiple operations in TensorFlow can be used to trigger a denial of service via 'CHECK'-fails (i.e., assertion failures). This is similar to CVE-2021-41197 and has a similar fix. It is possible that other similar instances exist.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qj5r-f9mv-rffh

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25676: When running versions prior to 2.12.0 and 2.11.1 with XLA, 'tf.raw_ops.ParallelConcat' segfaults with a nullptr dereference when given a parameter 'shape' with rank that is not greater than zero.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6wfh-89q8-44jq

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41200: In affected versions, if 'tf.summary.create_file_writer' is called with non-scalar arguments, code crashes due to a 'CHECK'-fail. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh8h-7j2j-qv4f

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41900: The security vulnerability results in FractionalMax(AVG)Pool with illegal pooling_ratio. Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or remote code execution.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvwp-h6jv-7472

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29197: Missing validation which causes denial of service via 'UnsortedSegmentJoin'.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29213: Crashes stemming from incomplete validation in signal ops.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35937: OOB read in 'Gather_nd' op in TF Lite.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pxrw-j2fv-hx3h

Tensorflow 1.15 includes a fix for CVE-2019-16778: In TensorFlow before 1.15, a heap buffer overflow in UnsortedSegmentSum can be produced when the Index template argument is int32. In this case data_size and num_segments fields are truncated from int64 to int32 and can produce negative numbers, resulting in accessing out of bounds heap memory. This is unlikely to be exploitable and was detected and fixed internally in TensorFlow 1.15 and 2.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-844w-j86r-4x2j
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2019-002.md
https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892

TensorFlow updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38546.

Tensorflow 1.15.0rc0 includes a fix for a potential security vulnerability where decoding variant tensors from proto could result in heap out of bounds memory access.
https://github.com/tensorflow/tensorflow/issues/37701

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 update its dependency "Apache Spark" to handle CVE-2018-11770.

The effect of CVE-2022-35991 was seen once more, where TensorListScatter and TensorListScatterV2 could potentially crash due to non scalar inputs in the element_shape parameter while in eager mode. This issue has been identified and resolved. The issue was identified when the following Python code was executed:

```python 
import tensorflow as tf 
arg_0=tf.random.uniform(shape=(2, 2, 2), dtype=tf.float16, maxval=None) 
arg_1=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536) 
arg_2=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536) 
arg_3='' 
tf.raw_ops.TensorListScatter(tensor=arg_0, indices=arg_1,  element_shape=arg_2, name=arg_3) 
```

A patch to resolve this issue is available in the GitHub commit bf9932fc907aff0e9e8cccf769e8b00d30fd81a1. This fix will be part of TensorFlow 2.11. Additionally, the commitment will be selected for TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these versions are also known to be affected and still under supported range.

For further details, please refer to TensorFlow's security guide. If there is any issue or question, contact us please.

The person who brought this vulnerability to our attention is Pattarakrit Rattankul.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35934: 'CHECK' failure in tf.reshape via overflows.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f4w6-h4f5-wx45

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25660: Prior to versions 2.12.0 and 2.11.1, when the parameter 'summarize' of 'tf.raw_ops.Print' is zero, the new method 'SummarizeArray<bool>' will reference to a nullptr, leading to a seg fault.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qjqc-vqcf-5qvj

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15207: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses 'ResolveAxis' to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the 'DCHECK' does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q4qf-3fc6-8x34

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25663: Prior to versions 2.12.0 and 2.11.1, when 'ctx->step_containter()' is a null ptr, the Lookup function will be executed with a null pointer.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-64jg-wjww-7c5w

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29212: Core dump when loading TFLite models with quantization.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36011: Null dereference on MLIR on empty function attributes.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fv43-93gv-vm8f

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27776.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35940: Int overflow in 'RaggedRangeOp'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x989-q2pq-4q5x

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15204: In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling "tf.raw_ops.GetSessionHandle" or "tf.raw_ops.GetSessionHandleV2" results in a null pointer dereference In linked snippet, in eager mode, "ctx->session_state()" returns "nullptr". Since code immediately dereferences this, we get a segmentation fault. The issue was patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1

TensorFlow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41225: In affected versions, TensorFlow's Grappler optimizer has a use of unitialized variable. If the 'train_nodes' vector (obtained from the saved model that gets optimized) does not contain a 'Dequeue' node, then 'dequeue_node' is left unitialized. The fix is also included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw
https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21736: The implementation of 'SparseTensorSliceDataset' has an undefined behavior: under certain conditions, it can be made to dereference a 'nullptr' value. The 3 input arguments to 'SparseTensorSliceDataset' represent a sparse tensor. However, there are some preconditions that these arguments must satisfy, but these are not validated in the implementation.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pfjj-m3jj-9jc9

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21726: The implementation of 'Dequantize' does not fully validate the value of 'axis' and can result in heap OOB accesses. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26270: In affected versions, running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25667: Prior to versions 2.12.0 and 2.11.1, integer overflow occurs when '2^31 <= num_frames * height * width * channels < 2^32', for example Full HD screencast of at least 346 frames.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqm2-gh8w-gr68

Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a `SavedModel` file (fixing the first one would trigger the same dereference in the second place). First, during constant folding, the `GraphDef` might not have the required nodes for the binary operation. If a node is missing, the correposning `mul_*child` would be null, and the dereference in the subsequent line would be incorrect. We have a similar issue during `IsIdentityConsumingSwitch`. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41894: The reference kernel of the 'CONV_3D_TRANSPOSE' TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of 'data_ptr += num_channels;' it should be 'data_ptr += output_num_channels;' as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6q3-vv32-2cq5

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36004: 'CHECK' fail in 'tf.random.gamma'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv8m-8x97-937q

Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Affected versions of Tensorflow allow tensors to have a large number of dimensions and each dimension can be as large as desired. However, the total number of elements in a tensor must fit within an 'int64_t'. If an overflow occurs, 'MultiplyWithoutOverflow' would return a negative result. In the majority of TensorFlow codebase this then results in a 'CHECK'-failure. Newer constructs exist which return a 'Status' instead of crashing the binary. This is a similar issue to CVE-2021-29584.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23580: During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-627q-g293-49q7

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 update its dependency "SQLite3" to handle CVE-2019-19880.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27780.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36012: Assertion fail on MLIR empty edge names.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jvhc-5hhr-w3v5

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35959: 'CHECK' failures in 'AvgPool3DGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wxjj-cgcx-r3vq

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-30115.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21727: The implementation of shape inference for 'Dequantize' is vulnerable to an integer overflow weakness. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes 'axis + 1', an attacker can trigger an integer overflow.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c6fh-56w7-fvjw

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29194: Missing validation which causes denial of service via 'DeleteSessionTensor'.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29196: Missing validation which causes denial of service via 'Conv3DBackpropFilterV2'.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41215: In affected versions, the shape inference code for 'DeserializeSparse' can trigger a null pointer dereference. This is because the shape inference function assumes that the 'serialize_sparse' tensor is a tensor with positive rank (and having '3' as the last dimension). The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x3v8-c8qx-3j3r
https://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23558: An attacker can craft a TFLite model that would cause an integer overflow in 'TfLiteIntArrayCreate'. The 'TfLiteIntArrayGetSizeInBytes' returns an 'int' instead of a 'size_t'. An attacker can control model inputs such that 'computed_size' overflows the size of 'int' datatype.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9gwq-6cwj-47h3

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21741: An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-428x-9xc2-m8mj

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "libjpeg-turbo" to handle CVE-2019-13960.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23582: A malicious user can cause a denial of service by altering a 'SavedModel' such that 'TensorByteSize' would trigger 'CHECK' failures. 'TensorShape' constructor throws a 'CHECK'-fail if shape is partial or has a number of elements that would overflow the size of an 'int'. The 'PartialTensorShape' constructor instead does not cause a 'CHECK'-abort if the shape is partial, which is exactly what this function needs to be able to return '-1'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4j82-5ccr-4r8v

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35997: 'CHECK' fail in 'tf.sparse.cross'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p7hr-f446-x6qf

TensorFlow 2.4.0 includes a fix for CVE-2020-15266: In Tensorflow before version 2.4.0, when the 'boxes' argument of 'tf.image.crop_and_resize' has a very large value, the CPU kernel implementation receives it as a C++ 'nan' floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault.
https://github.com/tensorflow/tensorflow/issues/42129
https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41209: In affected versions, the implementations for convolution operators trigger a division by 0 if passed empty filter tensor arguments. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hpv-v2rx-c5g6
https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'zlib' to v1.2.12 to handle CVE-2018-25032.

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "libjpeg-turbo" to handle CVE-2018-20330.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41885: When 'tf.raw_ops.FusedResizeAndPadConv2D' is given a large tensor shape, it overflows.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-762h-vpvw-3rcx

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41219: In affected versions, the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to 'nullptr'. This occurs whenever the dimensions of 'a' or 'b' are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, it should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x
https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41910: The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-frqp-wp83-qggv

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41207: In affected versions, the implementation of 'ParallelConcat' misses some input validation and can produce a division by 0. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7v94-64hj-m82h
https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235

Tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateOutputSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements. We can have a large enough number of dimensions in `output_shape.dim()` or just a small number of dimensions being large enough to cause an overflow in the multiplication. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29209: Type confusion leading to 'CHECK'-failure based denial of service.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22923.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41204: In affected versions, during TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-786j-5qwq-r36x
https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35964: Segfault in 'BlockLSTMGradV2'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f7r5-q7cx-h668

Tensorflow is an Open Source Machine Learning Framework. The implementation of `AssignOp` can result in copying uninitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Some versions of tensorflow are vulnerable to an out-of-bounds read issue, where decoding variant tensors from proto could lead to unauthorized heap memory access. The exploit maturity for this vulnerability is currently unproven.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27782.

TensorFlow 2.4.0 includes a fix for CVE-2020-15194: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1."

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41202: In affected versions, while calculating the size of the output within the 'tf.range' kernel, there is a conditional statement of type 'int64 = condition ? int64 : double'. Due to C++ implicit conversion rules, both branches of the condition will be cast to 'double' and the result would be truncated before the assignment. This result in overflows. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xrqm-fpgr-6hhx

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23562: The implementation of 'Range' suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qx3f-p745-w4hr

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "Apache Spark" to handle CVE-2018-17190.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41898: If 'SparseFillEmptyRowsGrad' is given empty inputs, TensorFlow will crash.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hq7g-wwwp-q46h

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22922.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29202: Denial of service in 'tf.ragged.constant' due to lack of validation.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35979: Segfault in 'QuantizedRelu' and 'QuantizedRelu6'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v7vw-577f-vp8x

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13435.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29193: missing validation which causes 'TensorSummaryV2' to crash.

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26268: In affected versions, the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25675: When running versions prior to 2.12.0 and 2.11.1 with XLA, 'tf.raw_ops.Bincount' segfaults when given a parameter 'weights' that is neither the same shape as parameter 'arr' nor a length-0 tensor.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7x4v-9gxg-9hwj

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25672: The function 'tf.raw_ops.LookupTableImportV2' cannot handle scalars in the 'values' parameter and gives an NPE. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29195: Missing validation which causes denial of service via 'StagePeek'.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35995: 'CHECK' fail in 'AudioSummaryV2'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g9h5-vr8m-x2h4

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41908: TensorFlow is an open source platform for machine learning. An input 'token' that is not a UTF-8 bytestring will trigger a 'CHECK' fail in 'tf.raw_ops.PyFunc'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv77-9g28-cwg3

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35965: Segfault in 'LowerBound' and 'UpperBound'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qxpx-j395-pw36

Tensorflow versions 1.15.2  and 2.0.1 updates its dependency "curl" to handle CVE-2019-5481.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15205: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'data_splits' argument of 'tf.raw_ops.StringNGrams' lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after 'ee ff' are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g7p5-5759-qv46

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41911: When printing a tensor, we get it's data as a 'const char*' array (since that's the underlying storage) and then we typecast it to the element type. However, conversions from 'char' to 'bool' are undefined if the 'char' is not '0' or '1', so sanitizers/fuzzers will crash.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pf36-r9c6-h97j

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that assertions in `function.cc` would be falsified and crash the Python interpreter. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 1.15.2 and 2.0.1 includes a fix for CVE-2020-5215: In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29199: Missing validation which causes denial of service via 'LoadAndRemapMatrix'.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41899: TensorFlow is an open source platform for machine learning. Inputs 'dense_features' or 'example_state_data' not of rank 2 will trigger a 'CHECK' fail in 'SdcaOptimizer'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-27rc-728f-x5w2

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-27579: Constructing a tflite model with a paramater 'filter_input_channel' of less than 1 gives a FPE.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5w96-866f-6rm8

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21730: The implementation of 'FractionalAvgPoolGrad' does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vjg4-v33c-ggc4

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23595: When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so 'flr->config_proto' is 'nullptr'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fpcp-9h7m-ffpx

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that Grappler optimizer would attempt to build a tensor using a reference `dtype`. This would result in a crash due to a `CHECK`-fail in the `Tensor` constructor as reference types are not allowed. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35982: Segfault in 'SparseBincount'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-397c-5g2j-qxpv

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41218: In affected versions, the shape inference code for 'AllToAll' can be made to execute a division by 0. This occurs whenever the 'split_count' argument is 0. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9crf-c6qr-r273
https://github.com/tensorflow/tensorflow/commit/a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41228: In affected versions, TensorFlow's 'saved_model_cli' tool is vulnerable to a code injection as it calls 'eval' on user supplied strings. This can be used by attackers to run arbitrary code on the plaform where the CLI tool runs. However, given that the tool is always run manually, the impact of this is not severe. The issue has been patched by adding a 'safe' flag which defaults to 'True' and an explicit warning for users.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3rcw-9p9x-582v
https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7

TensorFlow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0 includes a fix for CVE-2020-26266: In affected versions and under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36026: 'CHECK' fail in 'QuantizeAndDequantizeV3'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9cr2-8pwr-fhfq

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35998: 'CHECK' fail in 'EmptyTensorList'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhw4-wwr7-gjc5

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41895: If 'MirrorPadGrad' is given outsize input 'paddings', TensorFlow will give a heap OOB error.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gq2j-cr96-gvqx

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35988: 'CHECK' fail in 'tf.linalg.matrix_rank'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9vqj-64pv-w55c

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35983: 'CHECK' fail in 'Save' and 'SaveSlices'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6vp-8q9j-whx4

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41210: In affected versions, the shape inference functions for 'SparseCountSparseOutput' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m342-ff57-4jcc
https://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22926.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21729: The implementation of 'UnravelIndex' is vulnerable to a division by zero caused by an integer overflow bug.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-34f9-hjfq-rr8j

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26271: In affected versions, under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22924.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15206: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's "SavedModel" protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using "tensorflow-serving" or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d. However, this was not enough, as #41097 reported a different failure mode. The issue was finally patched in commit df095206f25471e864a8e63a0f1caef53a0e3a6

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13630.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29200: Missing validation which causes denial of service via 'LSTMBlockCell'.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36014: Null-dereference in 'mlir::tfg::TFOp::nameAttr'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7j3m-8g3c-9qqq

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35972: Segfault in 'QuantizedBiasAdd'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4pc4-m9mj-v2r9

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35963: 'CHECK' failures in 'FractionalAvgPoolGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-84jm-4cf3-9jfm

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36017: Segfault in 'Requantize'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wqmc-pm8c-2jhc

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41880: When the 'BaseCandidateSamplerOp' function receives a value in 'true_classes' larger than 'range_max', a heap oob read occurs.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8w5g-3wcv-9g2j

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35941: 'CHECK' failure in 'AvgPoolOp'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mgmh-g2v6-mqw5

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23563: In multiple places, TensorFlow uses 'tempfile.mktemp' to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in 'mktemp' and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness). In several instances, TensorFlow was supposed to actually create a temporary directory instead of a file. This logic bug is hidden away by the 'mktemp' function usage. It was replaced 'mktemp' with the safer 'mkstemp'/'mkdtemp' functions, according to the usage pattern.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wc4g-r73w-x8mm

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25670: Versions prior to 2.12.0 and 2.11.1 have a null point error in QuantizedMatMulWithBiasAndDequantize with MKL enabled.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rq-hwc3-x77w

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15211: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative "-1" value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the "-1" index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue was patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83). A potential workaround would be to add a custom "Verifier" to the model loading code to ensure that only operators which accept optional inputs use the "-1" special value and only for the tensors that they expect to be optional. Since this allow-list type approach is error-prone, it's advised upgrading to the patched code.

A vulnerability in TensorFlow's `GatherNd` function can trigger an out-of-bounds memory read or crash when inputs exceed output sizes. This issue is resolved in a GitHub commit, which will be included in an upcoming TensorFlow release. Additionally, the fix will be applied to several previous versions that are still under support. This vulnerability has no known workarounds, so updating to a patched version is recommended.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29216: Code injection in 'saved_model_cli'.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35993: 'CHECK' fail in 'SetSize'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wq6q-6m32-9rv9

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41897: If 'FractionMaxPoolGrad' is given outsize inputs 'row_pooling_sequence' and 'col_pooling_sequence', TensorFlow will crash.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2w8-jw48-fr7j

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23581: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'IsSimplifiableReshape' would trigger 'CHECK' failures.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq86-3f29-px2c

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35971: 'CHECK' fail in 'FakeQuantWithMinMaxVars'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9fpg-838v-wpv7

Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments, if the tensors have an invalid `dtype` and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 update its dependency "SQLite" to handle CVE-2020-11655.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23557: An attacker can craft a TFLite model that would trigger a division by zero in 'BiasAndClamp' implementation. There is no check that the 'bias_size' is non zero.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf2j-f278-xh4v

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36019: 'CHECK' fail in 'FakeQuantWithMinMaxVarsPerChannel'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9j4v-pp28-mxv7

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29211: Segfault when 'tf.histogram_fixed_width' is called with NaN values.

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "Apache Spark" to handle CVE-2019-10099.

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "SQLite" to handle CVE-2019-19645.

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41890: If 'BCast::ToShape' is given input larger than an 'int32', it will crash, despite being supposed to handle up to an 'int64'. An example can be seen in 'tf.experimental.numpy.outer' by passing in large input to the input 'b'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h246-cgh4-7475

TensorFlow is an open source platform for machine learning. An input 'sparse_matrix' that is not a matrix with a shape with rank 0 will trigger a 'CHECK' fail in 'tf.raw_ops.SparseMatrixNNZ'. We have patched the issue in GitHub commit f856d02e5322821aad155dad9b3acab1e9f5d693. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41227: In affected versions, the 'ImmutableConst' operation in TensorFlow can be tricked into reading arbitrary memory contents. This is because the 'tstring' TensorFlow string class has a special case for memory mapped strings but the operation itself does not offer any support for this datatype. The fix is also included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8c8-67vp-6mx7
https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b
https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15209: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a "nullptr" buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with "nullptr". However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue was patched in commit 0b5662bc.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35973: Segfault in 'QuantizedMatMul'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9v

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15203: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the 'fill' argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a 'printf' call is constructed. This may result in segmentation fault.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35968: 'CHECK' fail in 'AvgPoolGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2475-53vw-vp25

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21739: The implementation of 'QuantizedMaxPool' has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3mw4-6rj6-74g5

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29192: missing validation which crashes 'QuantizeAndDequantizeV4Grad'.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25664: Prior to versions 2.12.0 and 2.11.1, there is a heap buffer overflow in TAvgPoolGrad.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hg6-5c2q-7rcr

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35939: OOB write in 'scatter_nd' op in TF Lite.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-ffjm-4qwc-7cmf

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23560: An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvf-hxvg-f67v

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35990: 'CHECK' fail in 'FakeQuantWithMinMaxVarsPerChannelGradient'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h7ff-cfc9-wmmh

Tensorflow versions 1.15.2 and 2.0.1 updates 'sqlite3' to handle CVE-2019-16168.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25671: There is out-of-bounds access due to mismatched integer type sizes.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j5w9-hmfh-4cr6

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41212: In affected versions, the shape inference code for 'tf.ragged.cross' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fr77-rrx3-cp7g
https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21731: The implementation of shape inference for 'ConcatV2' can be used to trigger a denial of service attack via a segfault caused by a type confusion. The 'axis' argument is translated into 'concat_dim' in the 'ConcatShapeHelper' helper function. Then, a value for 'min_rank' is computed based on 'concat_dim'. This is then used to validate that the 'values' tensor has at least the required rank. However, 'WithRankAtLeast' receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that 'min_rank' is a 32-bits value and the value of 'axis', the 'rank' argument is a negative value, so the error check is bypassed.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m4hf-j54p-p353

TensorFlow 2.14.1 updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38545.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21728: The implementation of shape inference for 'ReverseSequence' does not fully validate the value of 'batch_dim' and can result in a heap OOB read. There is a check to make sure the value of 'batch_dim' does not go over the rank of the input, but there is no check for negative values. Negative dimensions are allowed in some cases to mimic Python's negative indexing (i.e., indexing from the end of the array), however if the value is too negative then the implementation of 'Dim' would access elements before the start of an array.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gmv-pjp9-p8w8

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2 and 2.3.2 update its dependency "PCRE" to handle CVE-2020-14155.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36027: Segfault TFLite converter on per-channel quantized transposed convolutions.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-79h2-q768-fpxr

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29204: Missing validation which causes denial of service via 'Conv3DBackpropFilterV2'.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23564: When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a 'CHECK' assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rcj-c8pj-v3m3

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36005: 'CHECK' fail in 'FakeQuantWithMinMaxVarsGradient'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r26c-679w-mrjm

Tensorflow versions 2.5.3, 2.6.3 and 2.7.1 include a fix for CVE-2021-41208: In affected versions, the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing 'nullptr's or via 'CHECK'-failures) as well as abuse undefined behavior (binding references to 'nullptr's). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. TensorFlow's boosted trees APIs will be deprecated in subsequent releases.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-57wx-m983-2f88
https://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6gw-r52c-724r

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23567: The implementations of 'Sparse*Cwise*' ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or 'CHECK'-fails when building new 'TensorShape' objects (so, assert failures based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrx2-r989-2c43

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41886: When 'tf.raw_ops.ImageProjectiveTransformV2' is given a large output shape, it overflows.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-54pp-c6pp-7fpx

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36003: 'CHECK' fail in 'RandomPoissonV2'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cv2p-32v3-vhwq

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41198: In affected versions, if 'tf.tile' is called with a large input argument, then the TensorFlow process will crash due to a 'CHECK'-failure caused by an overflow. The number of elements in the output tensor is too much for the 'int64_t' type and the overflow is detected via a 'CHECK' statement. This aborts the process. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2p25-55c9-h58q

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15195: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of 'SparseFillEmptyRowsGrad' uses a double indexing pattern. It is possible for 'reverse_index_map(i)' to be an index outside of bounds of 'grad_values', thus resulting in a heap buffer overflow.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35985: 'CHECK' fail in 'LRNGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9942-r22v-78cp

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29191: Missing validation which causes denial of service via 'GetSessionTensor'.

Tensorflow is an Open Source Machine Learning Framework. When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling `png::CommonInitDecode(..., &decode)`, the `decode` value contains allocated buffers which can only be freed by calling `png::CommonFreeDecode(&decode)`. However, several error case in the function implementation invoke the `OP_REQUIRES` macro which immediately terminates the execution of the function, without allowing for the memory free to occur. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21738: The implementation of 'SparseCountSparseOutput' can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4qx-4fjv-hmw6

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25674: Versions prior to 2.12.0 and 2.11.1 have a null pointer error in RandomShuffle with XLA enabled.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf97-q72m-7579

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25801: Prior to versions 2.12.0 and 2.11.1, 'nn_ops.fractional_avg_pool_v2' and 'nn_ops.fractional_max_pool_v2' require the first and fourth elements of their parameter 'pooling_ratio' to be equal to 1.0, as pooling on batch and channel dimensions is not supported.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f49c-87jh-g47q

TensorFlow 2.4.0 includes a fix for CVE-2020-15265: In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateTensorSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35974: Segfault in 'QuantizeDownAndShrinkRange'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vgvh-2pf4-jr2x

Tensorflow is an Open Source Machine Learning Framework. If a graph node is invalid, TensorFlow can leak memory in the implementation of `ImmutableExecutorState::Initialize`. Here, we set `item->kernel` to `nullptr` but it is a simple `OpKernel*` pointer so the memory that was previously allocated to it would leak. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27781.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23565: An attacker can trigger denial of service via assertion failure by altering a 'SavedModel' on disk such that 'AttrDef's of some operation are duplicated.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4v5p-v5h9-6xjx

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25668: Attackers using Tensorflow prior to 2.12.0 or 2.11.1 can access heap memory which is not in the control of user, leading to a crash or remote code execution.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gw97-ff7c-9v96

Tensorflow versions 1.15.2 and 2.0.1 update its dependency "SQLite" to handle CVE-2019-19646.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21732: The implementation of 'ThreadPoolHandle' can be used to trigger a denial of service attack by allocating too much memory. This is because the 'num_threads' argument is only checked to not be negative, but there is no upper bound on its value.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c582-c96p-r5cq

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23579: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'SafeToRemoveIdentity' would trigger 'CHECK' failures.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5f2r-qp73-37mr

Tensorflow is an Open Source Machine Learning Framework. TensorFlow is vulnerable to a heap OOB write in `Grappler`. The `set_output` function writes to an array at the specified index. Hence, this gives a malicious user a write primitive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41213: In affected versions, the code behind 'tf.function' API can be made to deadlock when two 'tf.function' decorated Python functions are mutually recursive. This occurs due to using a non-reentrant 'Lock' Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive 'tf.function', although this is not a frequent scenario.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf
https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15208: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a "DCHECK" which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue was patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d

Affected versions of Tensorflow are vulnerable to Integer Overflow. array_ops.upper_bound' causes a segfault when not given a rank 2 tensor. The flaw was fixed in May 30, 2023, but the CVE was published in July 30, 2024. It was noticed unpublished by the Safety CLI Cyber Security team.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41223: In affected versions, the implementation of 'FusedBatchNorm' kernels is vulnerable to a heap OOB access. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f54p-f6jp-4rhr
https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41884: If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. 
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jq6x-99hj-q636

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41217: In affected versions, the process of building the control flow graph for a TensorFlow model is vulnerable to a null pointer exception when nodes that should be paired are not. This occurs because the code assumes that the first node in the pairing (e.g., an 'Enter' node) always exists when encountering the second node (e.g., an 'Exit' node). When this is not the case, 'parent' is 'nullptr' so dereferencing it causes a crash. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5crj-c72x-m7gq
https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35952: 'CHECK' failures in 'UnbatchGradOp'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h5vq-gw2c-pq47

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41891: If 'tf.raw_ops.TensorListConcat' is given 'element_shape=[]', it results segmentation fault which can be used to trigger a denial of service attack.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-66vq-54fq-6jvv

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15202: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'Shard' API in TensorFlow expects the last argument to be a function taking two 'int64' (i.e., 'long long') arguments. However, there are several places in TensorFlow where a lambda taking 'int' or 'int32' arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6fg-mjxg-hqq4

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25659: Prior to versions 2.12.0 and 2.11.1, if the parameter 'indices' for 'DynamicStitch' does not match the shape of the parameter 'data', it can trigger an stack OOB read.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-93vr-9q9m-pj8p

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41889: If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a 'nullptr', which is not caught. An example can be seen in 'tf.compat.v1.extract_volume_patches' by passing in quantized tensors as input 'ksizes'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xxcj-rhqg-m46g

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41896: If 'ThreadUnsafeUnigramCandidateSampler' is given input 'filterbank_channel_count' greater than the allowed max size, TensorFlow will crash.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rmg2-f698-wq35

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36001: 'CHECK' fail in 'DrawBoundingBoxes'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jqm7-m5q7-3hm5

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35986: Segfault in 'RaggedBincount'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wr9v-g9vf-c74v

Tensorflow is an Open Source Machine Learning Framework. There is a typo in TensorFlow's `SpecializeType` which results in heap OOB read/write. Due to a typo, `arg` is initialized to the `i`th mutable argument in a loop where the loop index is `j`. Hence it is possible to assign to `arg` from outside the vector of arguments. Since this is a mutable proto value, it allows both read and write to outside of bounds data. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29201: Missing validation which results in undefined behavior in 'QuantizedConv2D'.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36002: 'CHECK' fail in 'Unbatch'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mh3m-62v7-68xg

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23568: The implementation of 'AddManySparseToTensorsMap' is vulnerable to an integer overflow which results in a 'CHECK'-fail when building new 'TensorShape' objects (so, an assert failure based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6445-fm66-fvq2

TensorFlow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41226: In affected versions, the implementation of 'SparseBinCount' is vulnerable to a heap OOB access. This is because of missing validation between the elements of the 'values' argument and the shape of the sparse output. The fix is also included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-374m-jm66-3vj8
https://github.com/tensorflow/tensorflow/commit/f410212e373eb2aec4c9e60bf3702eba99a38aba

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35984: 'CHECK' fail in 'ParameterizedTruncatedNormal'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2xf-8hgm-hpw5

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29206: Missing validation which results in undefined behavior in 'SparseTensorDenseAdd'.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21733: The implementation of 'StringNGrams' can be used to trigger a denial of service attack by causing an out of memory condition after an integer overflow. There is missing a validation on 'pad_witdh' and that result in computing a negative value for 'ngram_width' which is later used to allocate parts of the output.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98j8-c9q4-r38g

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35981: 'CHECK' fail in 'FractionalMaxPoolGrad'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vxv8-r8q2-63xw

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15190: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the "tf.raw_ops.Switch" operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is "nullptr", hence we are binding a reference to "nullptr". This is undefined behavior and reported as an error if compiling with "-fsanitize=null". In this case, this results in a segmentation fault The issue was patched in commit da8558533d925694483d2c136a9220d6d49d843c
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4g9f-63rx-5cw4

Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, TensorFlow can fail to specialize a type during shape inference. This case is covered by the 'DCHECK' function however, 'DCHECK' is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the 'ValueOrDie' line. This results in an assertion failure as 'ret' contains an error 'Status', not a value. In the second case we also get a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41196: In affected versions, the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m539-j985-hcr8

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a use after free behavior when decoding PNG images. After `png::CommonFreeDecode(&decode)` gets called, the values of `decode.width` and `decode.height` are in an unspecified state. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27774.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27779.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29207: Issues arising from undefined behavior stemming from users supplying invalid resource handles.

TensorFlow 2.8.4, 2.9.3, 2.10.1 and 2.11.0 include a fix for CVE-2022-35991: 'CHECK' fail in 'TensorListScatter' and 'TensorListScatterV2'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vm7x-4qhj-rrcq
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xf83-q765-xm6m

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a 'SavedModel' such that any binary op would trigger 'CHECK' failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the 'dtype' no longer matches the 'dtype' expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If 'Tin' and 'Tout' don't match the type of data in 'out' and 'input_*' tensors then 'flat<*>' would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a 'CHECK' crash, hence a denial of service. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41221: In affected versions, the shape inference code for the 'Cudnn*' operations can be tricked into accessing invalid memory via a heap buffer overflow. This occurs because the ranks of the 'input', 'input_h' and 'input_c' parameters are not validated, but code assumes they have certain values. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqv6-3phm-hcwx
https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41222: In affected versions, the implementation of 'SplitV' can trigger a segfault if an attacker supplies negative arguments. This occurs whenever 'size_splits' contains more than one value and at least one value is negative. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cpf4-wx82-gxp6
https://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41199: In affected versions, if 'tf.image.resize' is called with a large input argument then the TensorFlow process will crash due to a 'CHECK'-failure caused by an overflow. The number of elements in the output tensor is too much for the 'int64_t' type and the overflow is detected via a 'CHECK' statement. This aborts the process. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hx2-qx8j-qjqm

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21725: The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13871.

Tensorflow versions 2.5.3, 2.6.3 and 2.7.1 include a fix for CVE-2021-41206: In affected versions, several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or 'CHECK'-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. These issues were discovered internally via tooling while working on improving/testing GPU op determinism. As such, there aren't reproducers and there will be multiple fixes for these issues.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pgcq-h79j-2f69
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-43q8-3fv7-pr5x

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35966: Segfault in 'QuantizedAvgPool'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4w68-4x85-mjj9

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29208: Segfault and OOB write due to incomplete validation in 'EditDistance'.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22925.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41205: In affected versions, the shape inference functions for the 'QuantizeAndDequantizeV*' operations can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f
https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21737: The implementation of '*Bincount' operations allows malicious users to cause denial of service by passing in arguments which would trigger a 'CHECK'-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in 'CHECK' failures later when the output tensors get allocated.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2vv-v9cg-qhh7

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41224: In affected versions, the implementation of 'SparseFillEmptyRows' can be made to trigger a heap OOB access. This occurs whenever the size of 'indices' does not match the size of 'values'. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v
https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21740: The implementation of 'SparseCountSparseOutput' is vulnerable to a heap overflow.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-44qp-9wwf-734r

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35970: Segfault in 'QuantizedInstanceNorm'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g35r-369w-3fqp

Tensorflow is an Open Source Machine Learning Framework. The implementation of `GetInitOp` is vulnerable to a crash caused by dereferencing a null pointer. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25666: Prior to versions 2.12.0 and 2.11.1, there is a floating point exception in AudioSpectrogram. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f637-vh3r-vfh2

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23561: An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c78-vcq7-7vxq

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-22576.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35989: 'CHECK' fail in 'MaxPool'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j43h-pgmg-5hjq

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41888: When running on GPU, 'tf.image.generate_bounding_box_proposals' receives a 'scores' input that must be of rank 4 but is not checked.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6x99-gv2v-q76v

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21734: The implementation of 'MapStage' is vulnerable to a 'CHECK'-fail if the key tensor is not a scalar.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gcvh-66ff-4mwm

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35967: Segfault in 'QuantizedAdd'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6h3-348g-6h5x

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25661: In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the 'Convolution3DTranspose' function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a 'Convolution3DTranspose' call.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq

Tensorflow versions 1.15.2  and 2.0.1 updates its dependency "curl" to handle CVE-2019-5482.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35994: 'CHECK' fail in 'CollectiveGather'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fhfc-2q7x-929f

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21735: The implementation of 'FractionalMaxPool' can be made to crash a TensorFlow process via a division by 0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-87v6-crgm-2gfj

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36015: Integer overflow in math ops. 
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rh87-q4vg-m45j

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35969: 'CHECK' fail in 'Conv2DBackpropInput'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q2c3-jpmc-gfjx

Tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15210: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25669: Prior to versions 2.12.0 and 2.11.1, if the stride and window size are not positive for 'tf.raw_ops.AvgPoolGrad', it can give a floating point exception.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rcf8-g8jv-vg6p

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41893: If 'tf.raw_ops.TensorListResize' is given a nonscalar value for input 'size', it results 'CHECK' fail which can be used to trigger a denial of service attack.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-67pf-62xr-q35m

Tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 updates its dependency "Libjpeg-turbo" to handle CVE-2020-13790.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36016: 'CHECK'-fail in 'tensorflow::full_type::SubstituteFromAttrs'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g468-qj8g-vcjc

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25662: Versions prior to 2.12.0 and 2.11.1 are vulnerable to integer overflow in EditDistance.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7jvm-xxmr-v5cw

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35987: 'CHECK' fail in 'DenseBincount'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w62h-8xjm-fv49

Tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency "PCRE" to fix CVE-2019-20838.

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "SQLite3" to handle CVE-2019-19244.

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41201: In affected versions, during execution, 'EinsumHelper::ParseEquation()' is supposed to set the flags in 'input_has_ellipsis' vector and '*output_has_ellipsis' boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to 'true' and never assigns 'false'. This results in unitialized variable access if callers assume that 'EinsumHelper::ParseEquation()' always sets these flags. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j86v-p27c-73fm
https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6

Tensorflow 2.9.3 and 2.10.1 include a fix for CVE-2022-41887: 'tf.keras.losses.poisson' receives a 'y_pred' and 'y_true' that are passed through 'functor::mul' in 'BinaryOp'. If the resulting dimensions overflow an 'int32', TensorFlow will crash due to a size mismatch during broadcast assignment.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fvv-46hw-vpg3

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 update its dependency "SQLite" to handle CVE-2020-11656.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36000: 'CHECK' fail in 'Eig'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqxc-pvf8-2w9v

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23591: The 'GraphDef' format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a 'GraphDef' containing a fragment such as the following can be consumed when loading a 'SavedModel'. This would result in a stack overflow during execution as resolving each 'NodeDef' means resolving the function itself and its nodes.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-247x-2f9f-5wp7

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41909: An input 'encoded' that is not a valid 'CompositeTensorVariant' tensor will trigger a segfault in 'tf.raw_ops.CompositeTensorVariantToComponents'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjx6-v474-2ch9

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35996: Floating point exception in 'Conv2D'. 
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35960: 'CHECK' failure in 'TensorListReserve' via missing validation.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v5xg-3q2c-c2r4

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25673: Versions prior to 2.12.0 and 2.11.1 have a Floating Point Exception in TensorListSplit with XLA. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13631.

Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23559: An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both 'embedding_size' and 'lookup_size' are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41216: In affected versions, the shape inference function for 'Transpose' is vulnerable to a heap buffer overflow. This occurs whenever 'perm' contains negative elements. The shape inference function does not validate that the indices in 'perm' are all valid. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3ff2-r28g-w7h9
https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14

Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a `DCHECK`. However, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the dereferencing of the null pointer, whereas in the second case it results in a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36013: Null-dereference in 'mlir::tfg::GraphDefImporter::ConvertNodeDef'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-828c-5j5q-vrjq

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41203: In affected versions, an attacker can trigger undefined behavior, integer overflows, segfaults and 'CHECK'-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7pxj-m4jf-r6h2

Tensorflow versions 1.15.3, 2.0.2 and 2.1.1 updates its dependency "libjpeg-turbo" to handle CVE-2018-19664.

Affected versions of TensorFlow are susceptible to a Denial of Service (DoS) attack caused by an issue similar to CVE-2022-35991, occurring in TensorListScatter and TensorListScatterV2 when non-scalar inputs are used.

Tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency "SQLite" to handle CVE-2020-13434.

Tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency 'Junit4' to v4.13.1 to include a security fix.

Affected versions of Tensorflow are vulnerable to Denial of Service in the implementation of depthwise ops via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor. This is another instance of TFSA-2021-198 (CVE-2021-41197).

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41902: The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cg88-rpvp-cjv5

* Disputed * Scikit-learn 1.1.0rc1 includes a fix for CVE-2020-28975: svm_predict_values in svm.cpp in Libsvm v324, as used in scikit-learn and other products, allows attackers to cause a denial of service (segmentation fault) via a crafted model SVM (introduced via pickle, json, or any other model permanence standard) with a large value in the _n_support array. 
NOTE: the scikit-learn vendor's position is that the behavior can only occur if the library's API is violated by an application that changes a private attribute.

A sensitive data leakage vulnerability was identified in affected versions of scikit-learn TfidfVectorizer. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.

Scikit-learn 0.24.2 includes a fix for a ReDoS vulnerability.
https://github.com/scikit-learn/scikit-learn/issues/19522

An issue in Keras 3.7.0 allows attackers to write arbitrary files to the user's machine via downloading a crafted tar file through the get_file function.

Affected versions of the keras package are vulnerable to Deserialization of Untrusted Data due to the unconditional loading of external TensorFlow SavedModels during deserialization of .keras models in the TFSMLayer class. The flaw arises from the serialization of attacker-controlled file paths and the lack of validation in the from_config() method, which bypasses the safe_mode=True guarantee when a crafted .keras model is loaded. An attacker who supplies a malicious model can achieve arbitrary code execution during model inference under the victim's privileges.

A arbitrary code injection vulnerability in TensorFlow's Keras framework (<2.13) allows attackers to execute arbitrary code with the same permissions as the application using a model that allow arbitrary code irrespective of the application. See CVE-2024-3660.

Keras 2.6.0rc3 fixes a security issue for loading keras models via yaml, which could allow arbitrary code execution. See CVE-2021-37678.
https://github.com/keras-team/keras/commit/32febb6cdebe166fc70ad72f2da37607c13bb11a

Affected versions of the Keras package are vulnerable to Deserialization of Untrusted Data due to improper enforcement of safe mode protections in .keras model archives. The Model.load_model method can be bypassed even with safe_mode=True by crafting a malicious .keras archive containing a config.json file that invokes keras.config.enable_unsafe_deserialization() to disable safe mode before Lambda layers with arbitrary pickled code are processed. An attacker can exploit this by distributing a specially crafted .keras model that first disables safe mode protections through the configuration file and then executes arbitrary Python code via Lambda layers, achieving remote code execution despite the user's intention to load the model safely.

Affected versions of the Keras package are vulnerable to Information Disclosure and Server-Side Request Forgery (SSRF) due to insufficient restriction of the StringLookup.vocabulary path during model deserialization, even when safe_mode=True is used. The keras.models.load_model method will accept a crafted .keras archive that configures a StringLookup layer whose vocabulary points to arbitrary local or remote locations resolved via tf.io.gfile, causing file contents to be loaded into model state and retrievable via get_vocabulary, and enabling outbound fetches to attacker-chosen endpoints.

Pyyaml 5.3.1 includes a fix for CVE-2020-1747: A vulnerability was discovered in the PyYAML library in versions before 5.3.1, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. An attacker could use this flaw to execute arbitrary code on the system by abusing the python/object/new constructor.

PyYAML 5.1 through 5.1.2 has insufficient restrictions on the load and load_all functions because of a class deserialization issue, e.g., Popen is a class in the subprocess module. See CVE-2019-20477. NOTE: this issue exists because of an incomplete fix for CVE-2017-18342.

Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative values. 
NOTE: The vendor does not agree this is a vulnerability; the negative dimensions can only be created by an already privileged user (or internally).
https://github.com/numpy/numpy/issues/19000

Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service. 
NOTE: The vendor does not agree this is a vulnerability. In (very limited) circumstances a user may be able provoke the buffer overflow, the user is most likely already privileged to at least provoke denial of service by exhausting memory. Triggering this further requires the use of uncommon API (complicated structured dtypes), which is very unlikely to be available to an unprivileged user.
https://github.com/numpy/numpy/issues/18939

Numpy 1.22.0 includes a fix for CVE-2021-34141: An incomplete string comparison in the numpy.core component in NumPy before 1.22.0 allows attackers to trigger slightly incorrect copying by constructing specific string objects. 
NOTE: the vendor states that this reported code behavior is "completely harmless."
https://github.com/numpy/numpy/issues/18993

Numpy 1.22.2  includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. 
NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place.
NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.

Rasterio 1.3.8.post2 updates its bundled dependency 'libcurl' to v8.4.0 to include a security fix.
https://github.com/rasterio/rasterio-wheels/issues/112

Rasterio 1.3.8.post2 updates its bundled dependency 'libcurl' to v8.4.0 to include a security fix.

Rasterio 1.3.8.post1 updates its C dependency 'libwebp' to v1.3.2 to include a fix for a high-risk vulnerability.
https://github.com/rasterio/rasterio/discussions/2924

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'json-c' to include a security fix.
https://github.com/rasterio/rasterio-wheels/issues/81

* Disputed * Scikit-learn 1.1.0rc1 includes a fix for CVE-2020-28975: svm_predict_values in svm.cpp in Libsvm v324, as used in scikit-learn and other products, allows attackers to cause a denial of service (segmentation fault) via a crafted model SVM (introduced via pickle, json, or any other model permanence standard) with a large value in the _n_support array. 
NOTE: the scikit-learn vendor's position is that the behavior can only occur if the library's API is violated by an application that changes a private attribute.

A sensitive data leakage vulnerability was identified in affected versions of scikit-learn TfidfVectorizer. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.

Scikit-learn 0.24.2 includes a fix for a ReDoS vulnerability.
https://github.com/scikit-learn/scikit-learn/issues/19522

An issue in Keras 3.7.0 allows attackers to write arbitrary files to the user's machine via downloading a crafted tar file through the get_file function.

Affected versions of the keras package are vulnerable to Deserialization of Untrusted Data due to the unconditional loading of external TensorFlow SavedModels during deserialization of .keras models in the TFSMLayer class. The flaw arises from the serialization of attacker-controlled file paths and the lack of validation in the from_config() method, which bypasses the safe_mode=True guarantee when a crafted .keras model is loaded. An attacker who supplies a malicious model can achieve arbitrary code execution during model inference under the victim's privileges.

A arbitrary code injection vulnerability in TensorFlow's Keras framework (<2.13) allows attackers to execute arbitrary code with the same permissions as the application using a model that allow arbitrary code irrespective of the application. See CVE-2024-3660.

Keras 2.6.0rc3 fixes a security issue for loading keras models via yaml, which could allow arbitrary code execution. See CVE-2021-37678.
https://github.com/keras-team/keras/commit/32febb6cdebe166fc70ad72f2da37607c13bb11a

Affected versions of the Keras package are vulnerable to Deserialization of Untrusted Data due to improper enforcement of safe mode protections in .keras model archives. The Model.load_model method can be bypassed even with safe_mode=True by crafting a malicious .keras archive containing a config.json file that invokes keras.config.enable_unsafe_deserialization() to disable safe mode before Lambda layers with arbitrary pickled code are processed. An attacker can exploit this by distributing a specially crafted .keras model that first disables safe mode protections through the configuration file and then executes arbitrary Python code via Lambda layers, achieving remote code execution despite the user's intention to load the model safely.

Affected versions of the Keras package are vulnerable to Information Disclosure and Server-Side Request Forgery (SSRF) due to insufficient restriction of the StringLookup.vocabulary path during model deserialization, even when safe_mode=True is used. The keras.models.load_model method will accept a crafted .keras archive that configures a StringLookup layer whose vocabulary points to arbitrary local or remote locations resolved via tf.io.gfile, causing file contents to be loaded into model state and retrievable via get_vocabulary, and enabling outbound fetches to attacker-chosen endpoints.

Pyyaml 5.3.1 includes a fix for CVE-2020-1747: A vulnerability was discovered in the PyYAML library in versions before 5.3.1, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. An attacker could use this flaw to execute arbitrary code on the system by abusing the python/object/new constructor.

PyYAML 5.1 through 5.1.2 has insufficient restrictions on the load and load_all functions because of a class deserialization issue, e.g., Popen is a class in the subprocess module. See CVE-2019-20477. NOTE: this issue exists because of an incomplete fix for CVE-2017-18342.

Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative values. 
NOTE: The vendor does not agree this is a vulnerability; the negative dimensions can only be created by an already privileged user (or internally).
https://github.com/numpy/numpy/issues/19000

Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service. 
NOTE: The vendor does not agree this is a vulnerability. In (very limited) circumstances a user may be able provoke the buffer overflow, the user is most likely already privileged to at least provoke denial of service by exhausting memory. Triggering this further requires the use of uncommon API (complicated structured dtypes), which is very unlikely to be available to an unprivileged user.
https://github.com/numpy/numpy/issues/18939

Numpy 1.22.0 includes a fix for CVE-2021-34141: An incomplete string comparison in the numpy.core component in NumPy before 1.22.0 allows attackers to trigger slightly incorrect copying by constructing specific string objects. 
NOTE: the vendor states that this reported code behavior is "completely harmless."
https://github.com/numpy/numpy/issues/18993

Numpy 1.22.2  includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. 
NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place.
NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.

Rasterio 1.3.8.post2 updates its bundled dependency 'libcurl' to v8.4.0 to include a security fix.
https://github.com/rasterio/rasterio-wheels/issues/112

Rasterio 1.3.8.post2 updates its bundled dependency 'libcurl' to v8.4.0 to include a security fix.

Rasterio 1.3.8.post1 updates its C dependency 'libwebp' to v1.3.2 to include a fix for a high-risk vulnerability.
https://github.com/rasterio/rasterio/discussions/2924

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'hdf5' to include security fixes.
https://github.com/rasterio/rasterio-wheels/issues/81

Rasterio 1.3.0 updates its C dependency 'json-c' to include a security fix.
https://github.com/rasterio/rasterio-wheels/issues/81

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41214: In affected versions, the shape inference code for 'tf.ragged.cross' has an undefined behavior due to binding a reference to 'nullptr'. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vwhq-49r4-gj9v
https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35992: 'CHECK' fail in 'TensorListFromTensor'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9v8w-xmr4-wgxp

Tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41195: In affected versions, the implementation of 'tf.math.segment_*' operations results in a 'CHECK'-fail related abort (and denial of service) if a segment id in 'segment_ids' is large. This is similar to CVE-2021-29584 (and similar to other reported vulnerabilities in TensorFlow localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using 'AddDim'. However, if the number of elements in the tensor overflows an 'int64_t' value, 'AddDim' results in a 'CHECK' failure which provokes a 'std::abort'. Instead, code should use 'AddDimWithStatus'. The fix is included in TensorFlow 2.7.0.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cq76-mxrc-vchh

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35999: 'CHECK' fail in 'Conv2DBackpropInput'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-37jf-mjv6-xfqw

TensorFlow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36018: 'CHECK' fail in 'RaggedTensorToVariant'.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6cv-4fmf-66xf

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27775.

Tensorflow versions 2.5.3, 2.6.3 and 2.7.1 update its dependency 'icu' to v69.1 to include a security fix.

Tensorflow versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26267: In affected versions, the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes.

Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27778.

Tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25658: Prior to versions 2.12.0 and 2.11.1, an out of bounds read is in GRUBlockCellGrad.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-68v3-g9cm-rmm6

Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41907: When 'tf.raw_ops.ResizeNearestNeighborGrad' is given a large 'size' input, it overflows.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-368v-7v32-52fx

Affected versions of Tensorflow are vulnerable to Denial of Service via CHECK-failure (assertion failure) in constant folding. The output_prop tensor has a shape that is controlled by user input and this can result in triggering one of the CHECKs in the PartialTensorShape constructor. This is an instance of TFSA-2021-198 (CVE-2021-41197).