Safety CI > dssg/triage

pyup-scheduled-update-2024-02-01

1 year, 6 months ago

2f6089e4

Update wheel from 0.38.2 to 0.42.0

scipy 1.8.0 and scikit-learn 1.0.2 have known security vulnerabilities.

Package

Installed

Affected

Info

scikit-learn

1.0.2

<1.5.0

show

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

scikit-learn

1.0.2

<1.1.0rc1

show

* 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.

pyup-scheduled-update-2024-02-01

1 year, 6 months ago

6963385b

Update psycopg2-binary from 2.9.3 to 2.9.9

scipy 1.8.0 and scikit-learn 1.0.2 have known security vulnerabilities.

Package

Installed

Affected

Info

scikit-learn

1.0.2

<1.5.0

show

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

scikit-learn

1.0.2

<1.1.0rc1

show

* 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.

pyup-scheduled-update-2024-02-01

1 year, 6 months ago

cca2db37

Update pyyaml from 6.0 to 6.0.1

scipy 1.8.0 and scikit-learn 1.0.2 have known security vulnerabilities.

Package

Installed

Affected

Info

scikit-learn

1.0.2

<1.5.0

show

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

scikit-learn

1.0.2

<1.1.0rc1

show

* 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.

pyup-scheduled-update-2024-02-01

1 year, 6 months ago

c78b4b48

Update boto3 from 1.22.4 to 1.34.32

scipy 1.8.0 and scikit-learn 1.0.2 have known security vulnerabilities.

Package

Installed

Affected

Info

scikit-learn

1.0.2

<1.5.0

show

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

scikit-learn

1.0.2

<1.1.0rc1

show

* 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.

pyup-scheduled-update-2024-02-01

1 year, 6 months ago

df9cb77d

Update alembic from 1.7.7 to 1.13.1

scipy 1.8.0 and scikit-learn 1.0.2 have known security vulnerabilities.

Package

Installed

Affected

Info

scikit-learn

1.0.2

<1.5.0

show

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

scikit-learn

1.0.2

<1.1.0rc1

show

* 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.

pyup-scheduled-update-2024-02-01

1 year, 6 months ago

096c2cb1

Update black from 22.3.0 to 24.1.1

scipy 1.8.0 and scikit-learn 1.0.2 have known security vulnerabilities.

Package

Installed

Affected

Info

scikit-learn

1.0.2

<1.5.0

show

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

scikit-learn

1.0.2

<1.1.0rc1

show

* 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.

pyup-scheduled-update-2024-02-01

1 year, 6 months ago

1361d436

Update mkdocstrings from 0.18.1 to 0.24.0

scipy 1.8.0 and scikit-learn 1.0.2 have known security vulnerabilities.

Package

Installed

Affected

Info

scikit-learn

1.0.2

<1.5.0

show

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

scikit-learn

1.0.2

<1.1.0rc1

show

* 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.

pyup-scheduled-update-2024-02-01

1 year, 6 months ago

3faf2b8a

Update pymdown-extensions from 10.0.1 to 10.7

scipy 1.8.0, scikit-learn 1.0.2 and mkdocs-material 8.2.12 have known security vulnerabilities.

Package	Installed	Affected	Info
scikit-learn	1.0.2	<1.5.0	show A sensitive data leakage vulnerability was identified in affected versions of scikit-learn TfidfVectorizer. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.
scikit-learn	1.0.2	<1.1.0rc1	show * 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	8.2.12	<9.5.5	show 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	8.2.12	<9.1.13	show 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	8.2.12	<9.5.32	show MKDocs Material addresses an RXSS vulnerability found in deep links within search results.

pyup-scheduled-update-2024-02-01

1 year, 6 months ago

6c2f4a5a

Update mkdocs-material from 8.2.12 to 9.5.6

scipy 1.8.0 and scikit-learn 1.0.2 have known security vulnerabilities.

Package

Installed

Affected

Info

scikit-learn

1.0.2

<1.5.0

show

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

scikit-learn

1.0.2

<1.1.0rc1

show

* 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.

pyup-scheduled-update-2024-02-01

1 year, 6 months ago

773b1651

Update mkdocs from 1.3.0 to 1.5.3

scipy 1.8.0, scikit-learn 1.0.2 and mkdocs-material 8.2.12 have known security vulnerabilities.

Package	Installed	Affected	Info
scikit-learn	1.0.2	<1.5.0	show A sensitive data leakage vulnerability was identified in affected versions of scikit-learn TfidfVectorizer. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.
scikit-learn	1.0.2	<1.1.0rc1	show * 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	8.2.12	<9.5.5	show 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	8.2.12	<9.1.13	show 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	8.2.12	<9.5.32	show MKDocs Material addresses an RXSS vulnerability found in deep links within search results.

postmodeling_revision_2023

1 year, 6 months ago

248f963f

Merge branch 'dev' into postmodeling_revision_2023

scipy 1.8.0, scikit-learn 1.0.2 and mkdocs-material 8.2.12 have known security vulnerabilities.

Package	Installed	Affected	Info
scikit-learn	1.0.2	<1.5.0	show A sensitive data leakage vulnerability was identified in affected versions of scikit-learn TfidfVectorizer. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.
scikit-learn	1.0.2	<1.1.0rc1	show * 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	8.2.12	<9.5.5	show 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	8.2.12	<9.1.13	show 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	8.2.12	<9.5.32	show MKDocs Material addresses an RXSS vulnerability found in deep links within search results.

postmodeling_revision_2023

1 year, 6 months ago

bacaa906

Merge branch 'postmodeling_revision_2022' of github.com:dssg/triage into postmodeling_revision_2023

scipy 1.8.0, scikit-learn 1.0.2 and mkdocs-material 8.2.12 have known security vulnerabilities.

Package	Installed	Affected	Info
scikit-learn	1.0.2	<1.5.0	show A sensitive data leakage vulnerability was identified in affected versions of scikit-learn TfidfVectorizer. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.
scikit-learn	1.0.2	<1.1.0rc1	show * 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	8.2.12	<9.5.5	show 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	8.2.12	<9.1.13	show 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	8.2.12	<9.5.32	show MKDocs Material addresses an RXSS vulnerability found in deep links within search results.

master

1 year, 7 months ago

b8f57a82

github worfklow rule to trigger

scipy 1.8.0, scikit-learn 1.0.2 and mkdocs-material 8.2.12 have known security vulnerabilities.

Package	Installed	Affected	Info
scikit-learn	1.0.2	<1.5.0	show A sensitive data leakage vulnerability was identified in affected versions of scikit-learn TfidfVectorizer. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.
scikit-learn	1.0.2	<1.1.0rc1	show * 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	8.2.12	<9.5.5	show 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	8.2.12	<9.1.13	show 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	8.2.12	<9.5.32	show MKDocs Material addresses an RXSS vulnerability found in deep links within search results.

master

1 year, 7 months ago

0ef806f5

github workflow triggers when push a tag version

scipy 1.8.0, scikit-learn 1.0.2 and mkdocs-material 8.2.12 have known security vulnerabilities.

Package	Installed	Affected	Info
scikit-learn	1.0.2	<1.5.0	show A sensitive data leakage vulnerability was identified in affected versions of scikit-learn TfidfVectorizer. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.
scikit-learn	1.0.2	<1.1.0rc1	show * 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	8.2.12	<9.5.5	show 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	8.2.12	<9.1.13	show 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	8.2.12	<9.5.32	show MKDocs Material addresses an RXSS vulnerability found in deep links within search results.

master

1 year, 7 months ago

476b4de9

triage version

scipy 1.8.0, scikit-learn 1.0.2 and mkdocs-material 8.2.12 have known security vulnerabilities.

Package	Installed	Affected	Info
scikit-learn	1.0.2	<1.5.0	show A sensitive data leakage vulnerability was identified in affected versions of scikit-learn TfidfVectorizer. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.
scikit-learn	1.0.2	<1.1.0rc1	show * 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	8.2.12	<9.5.5	show 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	8.2.12	<9.1.13	show 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	8.2.12	<9.5.32	show MKDocs Material addresses an RXSS vulnerability found in deep links within search results.