Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings. It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon. If it is not possible to upgrade, we provide a separate package `pyarrow-hotfix` that disables the vulnerability on older PyArrow versions. See  https://pypi.org/project/pyarrow-hotfix/  for instructions.

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

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

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings. It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon. If it is not possible to upgrade, we provide a separate package `pyarrow-hotfix` that disables the vulnerability on older PyArrow versions. See  https://pypi.org/project/pyarrow-hotfix/  for instructions.

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

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

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings. It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon. If it is not possible to upgrade, we provide a separate package `pyarrow-hotfix` that disables the vulnerability on older PyArrow versions. See  https://pypi.org/project/pyarrow-hotfix/  for instructions.

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

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

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings. It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon. If it is not possible to upgrade, we provide a separate package `pyarrow-hotfix` that disables the vulnerability on older PyArrow versions. See  https://pypi.org/project/pyarrow-hotfix/  for instructions.

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

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

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings. It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon. If it is not possible to upgrade, we provide a separate package `pyarrow-hotfix` that disables the vulnerability on older PyArrow versions. See  https://pypi.org/project/pyarrow-hotfix/  for instructions.

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

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

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings. It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon. If it is not possible to upgrade, we provide a separate package `pyarrow-hotfix` that disables the vulnerability on older PyArrow versions. See  https://pypi.org/project/pyarrow-hotfix/  for instructions.

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

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

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings. It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon. If it is not possible to upgrade, we provide a separate package `pyarrow-hotfix` that disables the vulnerability on older PyArrow versions. See  https://pypi.org/project/pyarrow-hotfix/  for instructions.

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

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

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings. It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon. If it is not possible to upgrade, we provide a separate package `pyarrow-hotfix` that disables the vulnerability on older PyArrow versions. See  https://pypi.org/project/pyarrow-hotfix/  for instructions.

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

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

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings. It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon. If it is not possible to upgrade, we provide a separate package `pyarrow-hotfix` that disables the vulnerability on older PyArrow versions. See  https://pypi.org/project/pyarrow-hotfix/  for instructions.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings. It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon. If it is not possible to upgrade, we provide a separate package `pyarrow-hotfix` that disables the vulnerability on older PyArrow versions. See  https://pypi.org/project/pyarrow-hotfix/  for instructions.

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

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

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings. It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon. If it is not possible to upgrade, we provide a separate package `pyarrow-hotfix` that disables the vulnerability on older PyArrow versions. See  https://pypi.org/project/pyarrow-hotfix/  for instructions.

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

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

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings. It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon. If it is not possible to upgrade, we provide a separate package `pyarrow-hotfix` that disables the vulnerability on older PyArrow versions. See  https://pypi.org/project/pyarrow-hotfix/  for instructions.

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

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

Mkdocs-material 9.5.5 includes a change in its dependency on Pillow. Previously set to approximately version 9.4, it has now been updated to version 10.22. This change was made in response to the security vulnerability identified as CVE-2023-504477.
https://github.com/squidfunk/mkdocs-material/commit/fe11bc0cabd692d37bc4cc4e8034dbe6783ef36b

Mkdocs-material 9.1.13 includes a fix for a Race Condition Vulnerability. Social plugin triggers the race condition vulnerability when downloading fonts.
https://github.com/squidfunk/mkdocs-material/issues/5515
https://github.com/squidfunk/mkdocs-material/issues/5521

MKDocs Material addresses an RXSS vulnerability found in deep links within search results.

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

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

Mkdocs-material 9.5.5 includes a change in its dependency on Pillow. Previously set to approximately version 9.4, it has now been updated to version 10.22. This change was made in response to the security vulnerability identified as CVE-2023-504477.
https://github.com/squidfunk/mkdocs-material/commit/fe11bc0cabd692d37bc4cc4e8034dbe6783ef36b

Mkdocs-material 9.1.13 includes a fix for a Race Condition Vulnerability. Social plugin triggers the race condition vulnerability when downloading fonts.
https://github.com/squidfunk/mkdocs-material/issues/5515
https://github.com/squidfunk/mkdocs-material/issues/5521

MKDocs Material addresses an RXSS vulnerability found in deep links within search results.