** DISPUTED ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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

** DISPUTED ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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

** DISPUTED ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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.

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

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 ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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.

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

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 ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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.

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

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

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.

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

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.

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

** DISPUTED ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

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 1.16.3 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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

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

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.

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

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.

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

** DISPUTED ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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.

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

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

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.

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

** DISPUTED ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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 1.16.3 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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

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

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.

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

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.

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

** DISPUTED ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

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 1.16.3 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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

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

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.

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

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.

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

** DISPUTED ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

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 1.16.3 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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

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

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.

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

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.

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

** DISPUTED ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

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 1.16.3 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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

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

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.

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

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.

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

** DISPUTED ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

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 1.16.3 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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

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

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.

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

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.

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

** DISPUTED ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

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 1.16.3 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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

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

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.

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

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.

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

** DISPUTED ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

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 1.16.3 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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

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

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.

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

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.

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

** DISPUTED ** 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). NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely.

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. This is fixed in python-pip version 21.1.

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.

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.

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

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

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

Scipy 1.10.0rc1 includes a fix for a Denial of Service vulnerability.
https://github.com/scipy/scipy/issues/16235

Scipy 1.8.0 includes a fix for an Use After Free vulnerability.
https://github.com/scipy/scipy/issues/14713

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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

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 1.16.3 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 1.22.2  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.
https://github.com/numpy/numpy/issues/19038

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

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

Numpy 1.22.0 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

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.2.0 includes a fix for CVE-2022-21797: The package joblib from 0 and before 1.2.0 is vulnerable to Arbitrary Code Execution via the pre_dispatch flag in Parallel() class due to the eval() statement.
https://github.com/joblib/joblib/issues/1128

Joblib 1.2.0 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

Joblib 1.1.1 fixes a security issue where 'eval(pre_dispatch)' could potentially run arbitrary code. Now only basic numerics are supported.
https://github.com/joblib/joblib/pull/1327

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

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.

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

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.

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