scikit_learn-0.24.2-cp39-cp39-manylinux2010_x86_64.whl: 2 vulnerabilities (highest severity is: 7.5)

[mend-bolt-for-github[bot]]

Vulnerable Library - scikit_learn-0.24.2-cp39-cp39-manylinux2010_x86_64.whl

A set of python modules for machine learning and data mining

Library home page: https://files.pythonhosted.org/packages/04/e2/b43d4205124dd4c1f14606b2e2d78303db993c6653a90bf11dd0ffe23b5b/scikit_learn-0.24.2-cp39-cp39-manylinux2010_x86_64.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt

Found in HEAD commit: 2ed3e6d266405677eb45c15a472c288b604a1cad

Vulnerabilities

Vulnerability	Severity	CVSS	Dependency	Type	Fixed in (scikit_learn version)	Remediation Possible**
CVE-2020-28975	High	7.5	scikit_learn-0.24.2-cp39-cp39-manylinux2010_x86_64.whl	Direct	N/A	❌
CVE-2024-5206	Medium	4.7	scikit_learn-0.24.2-cp39-cp39-manylinux2010_x86_64.whl	Direct	1.5.0	❌

**In some cases, Remediation PR cannot be created automatically for a vulnerability despite the availability of remediation

Details

CVE-2020-28975

Vulnerable Library - scikit_learn-0.24.2-cp39-cp39-manylinux2010_x86_64.whl

A set of python modules for machine learning and data mining

Library home page: https://files.pythonhosted.org/packages/04/e2/b43d4205124dd4c1f14606b2e2d78303db993c6653a90bf11dd0ffe23b5b/scikit_learn-0.24.2-cp39-cp39-manylinux2010_x86_64.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt

Dependency Hierarchy:

• :x: scikit_learn-0.24.2-cp39-cp39-manylinux2010_x86_64.whl (Vulnerable Library)

Found in HEAD commit: 2ed3e6d266405677eb45c15a472c288b604a1cad

Found in base branch: main

Vulnerability Details

svm_predict_values in svm.cpp in Libsvm v324, as used in scikit-learn 0.23.2 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.

Publish Date: 2020-11-21

URL: CVE-2020-28975

CVSS 3 Score Details (7.5)

Base Score Metrics:

• Exploitability Metrics:
  • Attack Vector: Network
  • Attack Complexity: Low
  • Privileges Required: None
  • User Interaction: None
  • Scope: Unchanged
• Impact Metrics:
  • Confidentiality Impact: None
  • Integrity Impact: None
  • Availability Impact: High

For more information on CVSS3 Scores, click here.

Step up your Open Source Security Game with Mend here

CVE-2024-5206

Vulnerable Library - scikit_learn-0.24.2-cp39-cp39-manylinux2010_x86_64.whl

A set of python modules for machine learning and data mining

Library home page: https://files.pythonhosted.org/packages/04/e2/b43d4205124dd4c1f14606b2e2d78303db993c6653a90bf11dd0ffe23b5b/scikit_learn-0.24.2-cp39-cp39-manylinux2010_x86_64.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt

Dependency Hierarchy:

• :x: scikit_learn-0.24.2-cp39-cp39-manylinux2010_x86_64.whl (Vulnerable Library)

Found in HEAD commit: 2ed3e6d266405677eb45c15a472c288b604a1cad

Found in base branch: main

Vulnerability Details

A sensitive data leakage vulnerability was identified in scikit-learn's TfidfVectorizer, specifically in versions up to and including 1.4.1.post1, which was fixed in version 1.5.0. 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.

Publish Date: 2024-06-06

URL: CVE-2024-5206

CVSS 3 Score Details (4.7)

Base Score Metrics:

• Exploitability Metrics:
  • Attack Vector: Local
  • Attack Complexity: High
  • Privileges Required: Low
  • User Interaction: None
  • Scope: Unchanged
• Impact Metrics:
  • Confidentiality Impact: High
  • Integrity Impact: None
  • Availability Impact: None

For more information on CVSS3 Scores, click here.

Suggested Fix

Type: Upgrade version

Origin: https://www.cve.org/CVERecord?id=CVE-2024-5206

Release Date: 2024-06-06

Fix Resolution: 1.5.0

Step up your Open Source Security Game with Mend here