Affected versions of the pip package are vulnerable to Arbitrary File Overwrite due to improper validation of symbolic link targets in the fallback tar extraction code. In src/pip/_internal/utils/unpacking.py, the _untar_without_filter routine used when the Python tarfile module lacks PEP 706 (no tarfile.data_filter) extracted symlink members with tar._extract_member without verifying that link destinations resolve under the extraction root, a check later added via the is_symlink_target_in_tar helper.

A flaw was found in python-pip in the way it handled Unicode separators in git references. A remote attacker could possibly use this issue to install a different revision on a repository. The highest threat from this vulnerability is to data integrity.

An issue was discovered in Pip (all versions) because it installs the version with the highest version number, even if the user had intended to obtain a private package from a private index. This only affects use of the --extra-index-url option, and exploitation requires that the package does not already exist in the public index (and thus the attacker can put the package there with an arbitrary version number). A warning was added about this behavior in version 21.1.
NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

Pip 21.1 updates its dependency 'urllib3' to v1.26.4 due to security issues.

Versions of Pip prior to 19.2 are vulnerable to a directory traversal attack during the installation process from a URL. This vulnerability stems from improperly handling filenames in the Content-Disposition header that include path traversal sequences, potentially allowing unauthorized overwrite of critical files such as /root/.ssh/authorized_keys. The flaw is specifically found in the _download_http_url function within _internal/download.py.

Affected versions of the pip package are vulnerable to Path Traversal due to an incorrect directory containment check when extracting wheel archives. In src/pip/_internal/utils/unpacking.py, the is_within_directory() helper used os.path.commonprefix() (character-by-character) to compare directory and target paths, allowing crafted paths like a parent-directory substring match to be treated as safely inside the installation directory.

Affected versions of Pip are vulnerable to Command Injection. When installing a package from a Mercurial VCS URL (ie "pip install hg+...") with pip prior to v23.3, the specified Mercurial revision could be used to inject arbitrary configuration options to the "hg clone" call (ie "--config"). Controlling the Mercurial configuration can modify how and which repository is installed. This vulnerability does not affect users who aren't installing from Mercurial.

Pip solves a security vulnerability that previously allowed maliciously crafted wheel files to execute unauthorized code during installation.

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of the wheel package are vulnerable to Path Traversal due to applying extracted file permissions using an unsanitized archive pathname. The vulnerable logic is in wheel.cli.unpack.unpack (and setuptools._vendor.wheel.cli.unpack.unpack), where the code calls wf.extract(zinfo, destination) but then performs destination.joinpath(zinfo.filename).chmod(permissions) using zinfo.filename directly, allowing dot-dot-slash sequences to escape the intended directory.

Wheel 0.38.1 includes a fix for CVE-2022-40898: An issue discovered in Python Packaging Authority (PyPA) Wheel 0.37.1 and earlier allows remote attackers to cause a denial of service via attacker controlled input to wheel cli.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory.
https://github.com/sphinx-doc/sphinx/issues/8175
https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring.
https://github.com/sphinx-doc/sphinx/issues/8172
https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417

Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

* 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

* 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

Affected versions of the pip package are vulnerable to Arbitrary File Overwrite due to improper validation of symbolic link targets in the fallback tar extraction code. In src/pip/_internal/utils/unpacking.py, the _untar_without_filter routine used when the Python tarfile module lacks PEP 706 (no tarfile.data_filter) extracted symlink members with tar._extract_member without verifying that link destinations resolve under the extraction root, a check later added via the is_symlink_target_in_tar helper.

A flaw was found in python-pip in the way it handled Unicode separators in git references. A remote attacker could possibly use this issue to install a different revision on a repository. The highest threat from this vulnerability is to data integrity.

An issue was discovered in Pip (all versions) because it installs the version with the highest version number, even if the user had intended to obtain a private package from a private index. This only affects use of the --extra-index-url option, and exploitation requires that the package does not already exist in the public index (and thus the attacker can put the package there with an arbitrary version number). A warning was added about this behavior in version 21.1.
NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

Pip 21.1 updates its dependency 'urllib3' to v1.26.4 due to security issues.

Versions of Pip prior to 19.2 are vulnerable to a directory traversal attack during the installation process from a URL. This vulnerability stems from improperly handling filenames in the Content-Disposition header that include path traversal sequences, potentially allowing unauthorized overwrite of critical files such as /root/.ssh/authorized_keys. The flaw is specifically found in the _download_http_url function within _internal/download.py.

Affected versions of the pip package are vulnerable to Path Traversal due to an incorrect directory containment check when extracting wheel archives. In src/pip/_internal/utils/unpacking.py, the is_within_directory() helper used os.path.commonprefix() (character-by-character) to compare directory and target paths, allowing crafted paths like a parent-directory substring match to be treated as safely inside the installation directory.

Affected versions of Pip are vulnerable to Command Injection. When installing a package from a Mercurial VCS URL (ie "pip install hg+...") with pip prior to v23.3, the specified Mercurial revision could be used to inject arbitrary configuration options to the "hg clone" call (ie "--config"). Controlling the Mercurial configuration can modify how and which repository is installed. This vulnerability does not affect users who aren't installing from Mercurial.

Pip solves a security vulnerability that previously allowed maliciously crafted wheel files to execute unauthorized code during installation.

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

Dash 2.15.0 validates the URL to prevent XSS attacks identified on the 'dash-core-components'. 
https://github.com/plotly/dash/pull/2732

Earlier versions of Dash and its components are susceptible to an XSS vulnerability, specifically through the manipulation of the href attribute in a tags by an attacker. This flaw could potentially allow an authenticated attacker to access or manipulate user data and tokens, assuming the ability to store and present manipulated views to other users. The vulnerability notably requires the presence of user input storage mechanisms within Dash applications to be exploitable. Further details are covered under CVE-2024-21485.
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user. See CVE-2024-21485.

Dash 1.20.0 fixes a potential XSS vulnerability by starting to validate callback request fields.
https://github.com/plotly/dash/pull/1546

Dash 1.21.0 updates its dependency 'Plotly.js' to v2.2.1 to include a security fix.

Dash 2.15.0 validates the URL to prevent XSS attacks identified on the 'dash-core-components'. 
https://github.com/plotly/dash/pull/2732

Earlier versions of Dash and its components are susceptible to an XSS vulnerability, specifically through the manipulation of the href attribute in a tags by an attacker. This flaw could potentially allow an authenticated attacker to access or manipulate user data and tokens, assuming the ability to store and present manipulated views to other users. The vulnerability notably requires the presence of user input storage mechanisms within Dash applications to be exploitable. Further details are covered under CVE-2024-21485.
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user. See CVE-2024-21485.

Dash 1.20.0 fixes a potential XSS vulnerability by starting to validate callback request fields.
https://github.com/plotly/dash/pull/1546

Dash 1.21.0 updates its dependency 'Plotly.js' to v2.2.1 to include a security fix.

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of the wheel package are vulnerable to Path Traversal due to applying extracted file permissions using an unsanitized archive pathname. The vulnerable logic is in wheel.cli.unpack.unpack (and setuptools._vendor.wheel.cli.unpack.unpack), where the code calls wf.extract(zinfo, destination) but then performs destination.joinpath(zinfo.filename).chmod(permissions) using zinfo.filename directly, allowing dot-dot-slash sequences to escape the intended directory.

Wheel 0.38.1 includes a fix for CVE-2022-40898: An issue discovered in Python Packaging Authority (PyPA) Wheel 0.37.1 and earlier allows remote attackers to cause a denial of service via attacker controlled input to wheel cli.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory.
https://github.com/sphinx-doc/sphinx/issues/8175
https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring.
https://github.com/sphinx-doc/sphinx/issues/8172
https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417

Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

* 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

* 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

Dash-core-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-html-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-core-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-html-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Affected versions of the pip package are vulnerable to Arbitrary File Overwrite due to improper validation of symbolic link targets in the fallback tar extraction code. In src/pip/_internal/utils/unpacking.py, the _untar_without_filter routine used when the Python tarfile module lacks PEP 706 (no tarfile.data_filter) extracted symlink members with tar._extract_member without verifying that link destinations resolve under the extraction root, a check later added via the is_symlink_target_in_tar helper.

A flaw was found in python-pip in the way it handled Unicode separators in git references. A remote attacker could possibly use this issue to install a different revision on a repository. The highest threat from this vulnerability is to data integrity.

An issue was discovered in Pip (all versions) because it installs the version with the highest version number, even if the user had intended to obtain a private package from a private index. This only affects use of the --extra-index-url option, and exploitation requires that the package does not already exist in the public index (and thus the attacker can put the package there with an arbitrary version number). A warning was added about this behavior in version 21.1.
NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

Pip 21.1 updates its dependency 'urllib3' to v1.26.4 due to security issues.

Versions of Pip prior to 19.2 are vulnerable to a directory traversal attack during the installation process from a URL. This vulnerability stems from improperly handling filenames in the Content-Disposition header that include path traversal sequences, potentially allowing unauthorized overwrite of critical files such as /root/.ssh/authorized_keys. The flaw is specifically found in the _download_http_url function within _internal/download.py.

Affected versions of the pip package are vulnerable to Path Traversal due to an incorrect directory containment check when extracting wheel archives. In src/pip/_internal/utils/unpacking.py, the is_within_directory() helper used os.path.commonprefix() (character-by-character) to compare directory and target paths, allowing crafted paths like a parent-directory substring match to be treated as safely inside the installation directory.

Affected versions of Pip are vulnerable to Command Injection. When installing a package from a Mercurial VCS URL (ie "pip install hg+...") with pip prior to v23.3, the specified Mercurial revision could be used to inject arbitrary configuration options to the "hg clone" call (ie "--config"). Controlling the Mercurial configuration can modify how and which repository is installed. This vulnerability does not affect users who aren't installing from Mercurial.

Pip solves a security vulnerability that previously allowed maliciously crafted wheel files to execute unauthorized code during installation.

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

Dash 2.15.0 validates the URL to prevent XSS attacks identified on the 'dash-core-components'. 
https://github.com/plotly/dash/pull/2732

Earlier versions of Dash and its components are susceptible to an XSS vulnerability, specifically through the manipulation of the href attribute in a tags by an attacker. This flaw could potentially allow an authenticated attacker to access or manipulate user data and tokens, assuming the ability to store and present manipulated views to other users. The vulnerability notably requires the presence of user input storage mechanisms within Dash applications to be exploitable. Further details are covered under CVE-2024-21485.
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user. See CVE-2024-21485.

Dash 1.20.0 fixes a potential XSS vulnerability by starting to validate callback request fields.
https://github.com/plotly/dash/pull/1546

Dash 1.21.0 updates its dependency 'Plotly.js' to v2.2.1 to include a security fix.

Dash 2.15.0 validates the URL to prevent XSS attacks identified on the 'dash-core-components'. 
https://github.com/plotly/dash/pull/2732

Earlier versions of Dash and its components are susceptible to an XSS vulnerability, specifically through the manipulation of the href attribute in a tags by an attacker. This flaw could potentially allow an authenticated attacker to access or manipulate user data and tokens, assuming the ability to store and present manipulated views to other users. The vulnerability notably requires the presence of user input storage mechanisms within Dash applications to be exploitable. Further details are covered under CVE-2024-21485.
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user. See CVE-2024-21485.

Dash 1.20.0 fixes a potential XSS vulnerability by starting to validate callback request fields.
https://github.com/plotly/dash/pull/1546

Dash 1.21.0 updates its dependency 'Plotly.js' to v2.2.1 to include a security fix.

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of the wheel package are vulnerable to Path Traversal due to applying extracted file permissions using an unsanitized archive pathname. The vulnerable logic is in wheel.cli.unpack.unpack (and setuptools._vendor.wheel.cli.unpack.unpack), where the code calls wf.extract(zinfo, destination) but then performs destination.joinpath(zinfo.filename).chmod(permissions) using zinfo.filename directly, allowing dot-dot-slash sequences to escape the intended directory.

Wheel 0.38.1 includes a fix for CVE-2022-40898: An issue discovered in Python Packaging Authority (PyPA) Wheel 0.37.1 and earlier allows remote attackers to cause a denial of service via attacker controlled input to wheel cli.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory.
https://github.com/sphinx-doc/sphinx/issues/8175
https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring.
https://github.com/sphinx-doc/sphinx/issues/8172
https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417

Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

Plotly 4.9.0 builds Javascript extensions using Node 12 with an updated 'package-lock.json' that has many fewer security warnings.

Plotly 4.8.2 includes plotly.js version 1.54.5, which contains a security fix of a transitive dependency (ecstatic).

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

* 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

* 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

Dash-core-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-html-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-core-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-html-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Affected versions of the pip package are vulnerable to Arbitrary File Overwrite due to improper validation of symbolic link targets in the fallback tar extraction code. In src/pip/_internal/utils/unpacking.py, the _untar_without_filter routine used when the Python tarfile module lacks PEP 706 (no tarfile.data_filter) extracted symlink members with tar._extract_member without verifying that link destinations resolve under the extraction root, a check later added via the is_symlink_target_in_tar helper.

A flaw was found in python-pip in the way it handled Unicode separators in git references. A remote attacker could possibly use this issue to install a different revision on a repository. The highest threat from this vulnerability is to data integrity.

An issue was discovered in Pip (all versions) because it installs the version with the highest version number, even if the user had intended to obtain a private package from a private index. This only affects use of the --extra-index-url option, and exploitation requires that the package does not already exist in the public index (and thus the attacker can put the package there with an arbitrary version number). A warning was added about this behavior in version 21.1.
NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

Pip 21.1 updates its dependency 'urllib3' to v1.26.4 due to security issues.

Versions of Pip prior to 19.2 are vulnerable to a directory traversal attack during the installation process from a URL. This vulnerability stems from improperly handling filenames in the Content-Disposition header that include path traversal sequences, potentially allowing unauthorized overwrite of critical files such as /root/.ssh/authorized_keys. The flaw is specifically found in the _download_http_url function within _internal/download.py.

Affected versions of the pip package are vulnerable to Path Traversal due to an incorrect directory containment check when extracting wheel archives. In src/pip/_internal/utils/unpacking.py, the is_within_directory() helper used os.path.commonprefix() (character-by-character) to compare directory and target paths, allowing crafted paths like a parent-directory substring match to be treated as safely inside the installation directory.

Affected versions of Pip are vulnerable to Command Injection. When installing a package from a Mercurial VCS URL (ie "pip install hg+...") with pip prior to v23.3, the specified Mercurial revision could be used to inject arbitrary configuration options to the "hg clone" call (ie "--config"). Controlling the Mercurial configuration can modify how and which repository is installed. This vulnerability does not affect users who aren't installing from Mercurial.

Pip solves a security vulnerability that previously allowed maliciously crafted wheel files to execute unauthorized code during installation.

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of the wheel package are vulnerable to Path Traversal due to applying extracted file permissions using an unsanitized archive pathname. The vulnerable logic is in wheel.cli.unpack.unpack (and setuptools._vendor.wheel.cli.unpack.unpack), where the code calls wf.extract(zinfo, destination) but then performs destination.joinpath(zinfo.filename).chmod(permissions) using zinfo.filename directly, allowing dot-dot-slash sequences to escape the intended directory.

Wheel 0.38.1 includes a fix for CVE-2022-40898: An issue discovered in Python Packaging Authority (PyPA) Wheel 0.37.1 and earlier allows remote attackers to cause a denial of service via attacker controlled input to wheel cli.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory.
https://github.com/sphinx-doc/sphinx/issues/8175
https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring.
https://github.com/sphinx-doc/sphinx/issues/8172
https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417

Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

* 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

* 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

Dash-html-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-html-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Affected versions of the pip package are vulnerable to Arbitrary File Overwrite due to improper validation of symbolic link targets in the fallback tar extraction code. In src/pip/_internal/utils/unpacking.py, the _untar_without_filter routine used when the Python tarfile module lacks PEP 706 (no tarfile.data_filter) extracted symlink members with tar._extract_member without verifying that link destinations resolve under the extraction root, a check later added via the is_symlink_target_in_tar helper.

A flaw was found in python-pip in the way it handled Unicode separators in git references. A remote attacker could possibly use this issue to install a different revision on a repository. The highest threat from this vulnerability is to data integrity.

An issue was discovered in Pip (all versions) because it installs the version with the highest version number, even if the user had intended to obtain a private package from a private index. This only affects use of the --extra-index-url option, and exploitation requires that the package does not already exist in the public index (and thus the attacker can put the package there with an arbitrary version number). A warning was added about this behavior in version 21.1.
NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

Pip 21.1 updates its dependency 'urllib3' to v1.26.4 due to security issues.

Versions of Pip prior to 19.2 are vulnerable to a directory traversal attack during the installation process from a URL. This vulnerability stems from improperly handling filenames in the Content-Disposition header that include path traversal sequences, potentially allowing unauthorized overwrite of critical files such as /root/.ssh/authorized_keys. The flaw is specifically found in the _download_http_url function within _internal/download.py.

Affected versions of the pip package are vulnerable to Path Traversal due to an incorrect directory containment check when extracting wheel archives. In src/pip/_internal/utils/unpacking.py, the is_within_directory() helper used os.path.commonprefix() (character-by-character) to compare directory and target paths, allowing crafted paths like a parent-directory substring match to be treated as safely inside the installation directory.

Affected versions of Pip are vulnerable to Command Injection. When installing a package from a Mercurial VCS URL (ie "pip install hg+...") with pip prior to v23.3, the specified Mercurial revision could be used to inject arbitrary configuration options to the "hg clone" call (ie "--config"). Controlling the Mercurial configuration can modify how and which repository is installed. This vulnerability does not affect users who aren't installing from Mercurial.

Pip solves a security vulnerability that previously allowed maliciously crafted wheel files to execute unauthorized code during installation.

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

Dash 2.15.0 validates the URL to prevent XSS attacks identified on the 'dash-core-components'. 
https://github.com/plotly/dash/pull/2732

Earlier versions of Dash and its components are susceptible to an XSS vulnerability, specifically through the manipulation of the href attribute in a tags by an attacker. This flaw could potentially allow an authenticated attacker to access or manipulate user data and tokens, assuming the ability to store and present manipulated views to other users. The vulnerability notably requires the presence of user input storage mechanisms within Dash applications to be exploitable. Further details are covered under CVE-2024-21485.
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user. See CVE-2024-21485.

Dash 1.20.0 fixes a potential XSS vulnerability by starting to validate callback request fields.
https://github.com/plotly/dash/pull/1546

Dash 1.21.0 updates its dependency 'Plotly.js' to v2.2.1 to include a security fix.

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of the wheel package are vulnerable to Path Traversal due to applying extracted file permissions using an unsanitized archive pathname. The vulnerable logic is in wheel.cli.unpack.unpack (and setuptools._vendor.wheel.cli.unpack.unpack), where the code calls wf.extract(zinfo, destination) but then performs destination.joinpath(zinfo.filename).chmod(permissions) using zinfo.filename directly, allowing dot-dot-slash sequences to escape the intended directory.

Wheel 0.38.1 includes a fix for CVE-2022-40898: An issue discovered in Python Packaging Authority (PyPA) Wheel 0.37.1 and earlier allows remote attackers to cause a denial of service via attacker controlled input to wheel cli.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory.
https://github.com/sphinx-doc/sphinx/issues/8175
https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring.
https://github.com/sphinx-doc/sphinx/issues/8172
https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417

Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

* 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

* 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

Dash-core-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-html-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-core-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-html-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Affected versions of the pip package are vulnerable to Arbitrary File Overwrite due to improper validation of symbolic link targets in the fallback tar extraction code. In src/pip/_internal/utils/unpacking.py, the _untar_without_filter routine used when the Python tarfile module lacks PEP 706 (no tarfile.data_filter) extracted symlink members with tar._extract_member without verifying that link destinations resolve under the extraction root, a check later added via the is_symlink_target_in_tar helper.

A flaw was found in python-pip in the way it handled Unicode separators in git references. A remote attacker could possibly use this issue to install a different revision on a repository. The highest threat from this vulnerability is to data integrity.

An issue was discovered in Pip (all versions) because it installs the version with the highest version number, even if the user had intended to obtain a private package from a private index. This only affects use of the --extra-index-url option, and exploitation requires that the package does not already exist in the public index (and thus the attacker can put the package there with an arbitrary version number). A warning was added about this behavior in version 21.1.
NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

Pip 21.1 updates its dependency 'urllib3' to v1.26.4 due to security issues.

Versions of Pip prior to 19.2 are vulnerable to a directory traversal attack during the installation process from a URL. This vulnerability stems from improperly handling filenames in the Content-Disposition header that include path traversal sequences, potentially allowing unauthorized overwrite of critical files such as /root/.ssh/authorized_keys. The flaw is specifically found in the _download_http_url function within _internal/download.py.

Affected versions of the pip package are vulnerable to Path Traversal due to an incorrect directory containment check when extracting wheel archives. In src/pip/_internal/utils/unpacking.py, the is_within_directory() helper used os.path.commonprefix() (character-by-character) to compare directory and target paths, allowing crafted paths like a parent-directory substring match to be treated as safely inside the installation directory.

Affected versions of Pip are vulnerable to Command Injection. When installing a package from a Mercurial VCS URL (ie "pip install hg+...") with pip prior to v23.3, the specified Mercurial revision could be used to inject arbitrary configuration options to the "hg clone" call (ie "--config"). Controlling the Mercurial configuration can modify how and which repository is installed. This vulnerability does not affect users who aren't installing from Mercurial.

Pip solves a security vulnerability that previously allowed maliciously crafted wheel files to execute unauthorized code during installation.

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of the wheel package are vulnerable to Path Traversal due to applying extracted file permissions using an unsanitized archive pathname. The vulnerable logic is in wheel.cli.unpack.unpack (and setuptools._vendor.wheel.cli.unpack.unpack), where the code calls wf.extract(zinfo, destination) but then performs destination.joinpath(zinfo.filename).chmod(permissions) using zinfo.filename directly, allowing dot-dot-slash sequences to escape the intended directory.

Wheel 0.38.1 includes a fix for CVE-2022-40898: An issue discovered in Python Packaging Authority (PyPA) Wheel 0.37.1 and earlier allows remote attackers to cause a denial of service via attacker controlled input to wheel cli.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory.
https://github.com/sphinx-doc/sphinx/issues/8175
https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring.
https://github.com/sphinx-doc/sphinx/issues/8172
https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417

Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

* 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

* 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

Dash-html-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-core-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-html-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Affected versions of the pip package are vulnerable to Arbitrary File Overwrite due to improper validation of symbolic link targets in the fallback tar extraction code. In src/pip/_internal/utils/unpacking.py, the _untar_without_filter routine used when the Python tarfile module lacks PEP 706 (no tarfile.data_filter) extracted symlink members with tar._extract_member without verifying that link destinations resolve under the extraction root, a check later added via the is_symlink_target_in_tar helper.

A flaw was found in python-pip in the way it handled Unicode separators in git references. A remote attacker could possibly use this issue to install a different revision on a repository. The highest threat from this vulnerability is to data integrity.

An issue was discovered in Pip (all versions) because it installs the version with the highest version number, even if the user had intended to obtain a private package from a private index. This only affects use of the --extra-index-url option, and exploitation requires that the package does not already exist in the public index (and thus the attacker can put the package there with an arbitrary version number). A warning was added about this behavior in version 21.1.
NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

Pip 21.1 updates its dependency 'urllib3' to v1.26.4 due to security issues.

Versions of Pip prior to 19.2 are vulnerable to a directory traversal attack during the installation process from a URL. This vulnerability stems from improperly handling filenames in the Content-Disposition header that include path traversal sequences, potentially allowing unauthorized overwrite of critical files such as /root/.ssh/authorized_keys. The flaw is specifically found in the _download_http_url function within _internal/download.py.

Affected versions of the pip package are vulnerable to Path Traversal due to an incorrect directory containment check when extracting wheel archives. In src/pip/_internal/utils/unpacking.py, the is_within_directory() helper used os.path.commonprefix() (character-by-character) to compare directory and target paths, allowing crafted paths like a parent-directory substring match to be treated as safely inside the installation directory.

Affected versions of Pip are vulnerable to Command Injection. When installing a package from a Mercurial VCS URL (ie "pip install hg+...") with pip prior to v23.3, the specified Mercurial revision could be used to inject arbitrary configuration options to the "hg clone" call (ie "--config"). Controlling the Mercurial configuration can modify how and which repository is installed. This vulnerability does not affect users who aren't installing from Mercurial.

Pip solves a security vulnerability that previously allowed maliciously crafted wheel files to execute unauthorized code during installation.

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of the wheel package are vulnerable to Path Traversal due to applying extracted file permissions using an unsanitized archive pathname. The vulnerable logic is in wheel.cli.unpack.unpack (and setuptools._vendor.wheel.cli.unpack.unpack), where the code calls wf.extract(zinfo, destination) but then performs destination.joinpath(zinfo.filename).chmod(permissions) using zinfo.filename directly, allowing dot-dot-slash sequences to escape the intended directory.

Wheel 0.38.1 includes a fix for CVE-2022-40898: An issue discovered in Python Packaging Authority (PyPA) Wheel 0.37.1 and earlier allows remote attackers to cause a denial of service via attacker controlled input to wheel cli.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory.
https://github.com/sphinx-doc/sphinx/issues/8175
https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring.
https://github.com/sphinx-doc/sphinx/issues/8172
https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417

Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

* 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

* 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

Dash-core-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-html-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-core-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Dash-html-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Affected versions of the pip package are vulnerable to Arbitrary File Overwrite due to improper validation of symbolic link targets in the fallback tar extraction code. In src/pip/_internal/utils/unpacking.py, the _untar_without_filter routine used when the Python tarfile module lacks PEP 706 (no tarfile.data_filter) extracted symlink members with tar._extract_member without verifying that link destinations resolve under the extraction root, a check later added via the is_symlink_target_in_tar helper.

A flaw was found in python-pip in the way it handled Unicode separators in git references. A remote attacker could possibly use this issue to install a different revision on a repository. The highest threat from this vulnerability is to data integrity.

An issue was discovered in Pip (all versions) because it installs the version with the highest version number, even if the user had intended to obtain a private package from a private index. This only affects use of the --extra-index-url option, and exploitation requires that the package does not already exist in the public index (and thus the attacker can put the package there with an arbitrary version number). A warning was added about this behavior in version 21.1.
NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

Pip 21.1 updates its dependency 'urllib3' to v1.26.4 due to security issues.

Versions of Pip prior to 19.2 are vulnerable to a directory traversal attack during the installation process from a URL. This vulnerability stems from improperly handling filenames in the Content-Disposition header that include path traversal sequences, potentially allowing unauthorized overwrite of critical files such as /root/.ssh/authorized_keys. The flaw is specifically found in the _download_http_url function within _internal/download.py.

Affected versions of the pip package are vulnerable to Path Traversal due to an incorrect directory containment check when extracting wheel archives. In src/pip/_internal/utils/unpacking.py, the is_within_directory() helper used os.path.commonprefix() (character-by-character) to compare directory and target paths, allowing crafted paths like a parent-directory substring match to be treated as safely inside the installation directory.

Affected versions of Pip are vulnerable to Command Injection. When installing a package from a Mercurial VCS URL (ie "pip install hg+...") with pip prior to v23.3, the specified Mercurial revision could be used to inject arbitrary configuration options to the "hg clone" call (ie "--config"). Controlling the Mercurial configuration can modify how and which repository is installed. This vulnerability does not affect users who aren't installing from Mercurial.

Pip solves a security vulnerability that previously allowed maliciously crafted wheel files to execute unauthorized code during installation.

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of the wheel package are vulnerable to Path Traversal due to applying extracted file permissions using an unsanitized archive pathname. The vulnerable logic is in wheel.cli.unpack.unpack (and setuptools._vendor.wheel.cli.unpack.unpack), where the code calls wf.extract(zinfo, destination) but then performs destination.joinpath(zinfo.filename).chmod(permissions) using zinfo.filename directly, allowing dot-dot-slash sequences to escape the intended directory.

Wheel 0.38.1 includes a fix for CVE-2022-40898: An issue discovered in Python Packaging Authority (PyPA) Wheel 0.37.1 and earlier allows remote attackers to cause a denial of service via attacker controlled input to wheel cli.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory.
https://github.com/sphinx-doc/sphinx/issues/8175
https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring.
https://github.com/sphinx-doc/sphinx/issues/8172
https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417

Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

* 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

* 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

Affected versions of the pip package are vulnerable to Arbitrary File Overwrite due to improper validation of symbolic link targets in the fallback tar extraction code. In src/pip/_internal/utils/unpacking.py, the _untar_without_filter routine used when the Python tarfile module lacks PEP 706 (no tarfile.data_filter) extracted symlink members with tar._extract_member without verifying that link destinations resolve under the extraction root, a check later added via the is_symlink_target_in_tar helper.

A flaw was found in python-pip in the way it handled Unicode separators in git references. A remote attacker could possibly use this issue to install a different revision on a repository. The highest threat from this vulnerability is to data integrity.

An issue was discovered in Pip (all versions) because it installs the version with the highest version number, even if the user had intended to obtain a private package from a private index. This only affects use of the --extra-index-url option, and exploitation requires that the package does not already exist in the public index (and thus the attacker can put the package there with an arbitrary version number). A warning was added about this behavior in version 21.1.
NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

Pip 21.1 updates its dependency 'urllib3' to v1.26.4 due to security issues.

Versions of Pip prior to 19.2 are vulnerable to a directory traversal attack during the installation process from a URL. This vulnerability stems from improperly handling filenames in the Content-Disposition header that include path traversal sequences, potentially allowing unauthorized overwrite of critical files such as /root/.ssh/authorized_keys. The flaw is specifically found in the _download_http_url function within _internal/download.py.

Affected versions of the pip package are vulnerable to Path Traversal due to an incorrect directory containment check when extracting wheel archives. In src/pip/_internal/utils/unpacking.py, the is_within_directory() helper used os.path.commonprefix() (character-by-character) to compare directory and target paths, allowing crafted paths like a parent-directory substring match to be treated as safely inside the installation directory.

Affected versions of Pip are vulnerable to Command Injection. When installing a package from a Mercurial VCS URL (ie "pip install hg+...") with pip prior to v23.3, the specified Mercurial revision could be used to inject arbitrary configuration options to the "hg clone" call (ie "--config"). Controlling the Mercurial configuration can modify how and which repository is installed. This vulnerability does not affect users who aren't installing from Mercurial.

Pip solves a security vulnerability that previously allowed maliciously crafted wheel files to execute unauthorized code during installation.

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of the wheel package are vulnerable to Path Traversal due to applying extracted file permissions using an unsanitized archive pathname. The vulnerable logic is in wheel.cli.unpack.unpack (and setuptools._vendor.wheel.cli.unpack.unpack), where the code calls wf.extract(zinfo, destination) but then performs destination.joinpath(zinfo.filename).chmod(permissions) using zinfo.filename directly, allowing dot-dot-slash sequences to escape the intended directory.

Wheel 0.38.1 includes a fix for CVE-2022-40898: An issue discovered in Python Packaging Authority (PyPA) Wheel 0.37.1 and earlier allows remote attackers to cause a denial of service via attacker controlled input to wheel cli.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory.
https://github.com/sphinx-doc/sphinx/issues/8175
https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring.
https://github.com/sphinx-doc/sphinx/issues/8172
https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417

Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

* 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

* 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

Affected versions of the pip package are vulnerable to Arbitrary File Overwrite due to improper validation of symbolic link targets in the fallback tar extraction code. In src/pip/_internal/utils/unpacking.py, the _untar_without_filter routine used when the Python tarfile module lacks PEP 706 (no tarfile.data_filter) extracted symlink members with tar._extract_member without verifying that link destinations resolve under the extraction root, a check later added via the is_symlink_target_in_tar helper.

A flaw was found in python-pip in the way it handled Unicode separators in git references. A remote attacker could possibly use this issue to install a different revision on a repository. The highest threat from this vulnerability is to data integrity.

An issue was discovered in Pip (all versions) because it installs the version with the highest version number, even if the user had intended to obtain a private package from a private index. This only affects use of the --extra-index-url option, and exploitation requires that the package does not already exist in the public index (and thus the attacker can put the package there with an arbitrary version number). A warning was added about this behavior in version 21.1.
NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

Pip 21.1 updates its dependency 'urllib3' to v1.26.4 due to security issues.

Versions of Pip prior to 19.2 are vulnerable to a directory traversal attack during the installation process from a URL. This vulnerability stems from improperly handling filenames in the Content-Disposition header that include path traversal sequences, potentially allowing unauthorized overwrite of critical files such as /root/.ssh/authorized_keys. The flaw is specifically found in the _download_http_url function within _internal/download.py.

Affected versions of the pip package are vulnerable to Path Traversal due to an incorrect directory containment check when extracting wheel archives. In src/pip/_internal/utils/unpacking.py, the is_within_directory() helper used os.path.commonprefix() (character-by-character) to compare directory and target paths, allowing crafted paths like a parent-directory substring match to be treated as safely inside the installation directory.

Affected versions of Pip are vulnerable to Command Injection. When installing a package from a Mercurial VCS URL (ie "pip install hg+...") with pip prior to v23.3, the specified Mercurial revision could be used to inject arbitrary configuration options to the "hg clone" call (ie "--config"). Controlling the Mercurial configuration can modify how and which repository is installed. This vulnerability does not affect users who aren't installing from Mercurial.

Pip solves a security vulnerability that previously allowed maliciously crafted wheel files to execute unauthorized code during installation.

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'.
https://github.com/pallets/click/issues/1752

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of the wheel package are vulnerable to Path Traversal due to applying extracted file permissions using an unsanitized archive pathname. The vulnerable logic is in wheel.cli.unpack.unpack (and setuptools._vendor.wheel.cli.unpack.unpack), where the code calls wf.extract(zinfo, destination) but then performs destination.joinpath(zinfo.filename).chmod(permissions) using zinfo.filename directly, allowing dot-dot-slash sequences to escape the intended directory.

Wheel 0.38.1 includes a fix for CVE-2022-40898: An issue discovered in Python Packaging Authority (PyPA) Wheel 0.37.1 and earlier allows remote attackers to cause a denial of service via attacker controlled input to wheel cli.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory.
https://github.com/sphinx-doc/sphinx/issues/8175
https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring.
https://github.com/sphinx-doc/sphinx/issues/8172
https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417

Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

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

*Disputed* Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call.
NOTE: Third parties dispute this issue because it is  a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated  sources.
https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb

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 1.22.2  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.

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

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Affected versions of Joblib are vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

* 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

* 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

Dash-html-components affected versions are vulnerable to Cross-site Scripting (XSS) when the href of the a tag is controlled by an adversary. An authenticated attacker who stores a view that exploits this vulnerability could steal the data that's visible to another user who opens that view - not just the data already included on the page, but they could also, in theory, make additional requests and access other data accessible to this user. In some cases, they could also steal the access tokens of that user, which would allow the attacker to act as that user, including viewing other apps and resources hosted on the same server. 
#Note: This is only exploitable in Dash apps that include some mechanism to store user input to be reloaded by a different user.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory.
https://github.com/sphinx-doc/sphinx/issues/8175
https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring.
https://github.com/sphinx-doc/sphinx/issues/8172
https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417

Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

Affected versions of the wheel package are vulnerable to Path Traversal due to applying extracted file permissions using an unsanitized archive pathname. The vulnerable logic is in wheel.cli.unpack.unpack (and setuptools._vendor.wheel.cli.unpack.unpack), where the code calls wf.extract(zinfo, destination) but then performs destination.joinpath(zinfo.filename).chmod(permissions) using zinfo.filename directly, allowing dot-dot-slash sequences to escape the intended directory.

Wheel 0.38.1 includes a fix for CVE-2022-40898: An issue discovered in Python Packaging Authority (PyPA) Wheel 0.37.1 and earlier allows remote attackers to cause a denial of service via attacker controlled input to wheel cli.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory.
https://github.com/sphinx-doc/sphinx/issues/8175
https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2

Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring.
https://github.com/sphinx-doc/sphinx/issues/8172
https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417

Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst