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.
A set of python modules for machine learning and data mining
Library home page: https://files.pythonhosted.org/packages/bd/05/e561bc99a615b5c099c7a9355409e5e57c525a108f1c2e156abb005b90a6/scikit_learn-1.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Path to dependency file: /Day2/Creating DS ENV/requirements.txt
Path to vulnerable library: /Day2/Creating DS ENV/requirements.txt
Found in HEAD commit: 5830f411b03412c02b023ec7b8edb5c1934d18b0
Vulnerabilities
**In some cases, Remediation PR cannot be created automatically for a vulnerability despite the availability of remediation
Details
CVE-2024-5206
### Vulnerable Library - scikit_learn-1.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whlA set of python modules for machine learning and data mining
Library home page: https://files.pythonhosted.org/packages/bd/05/e561bc99a615b5c099c7a9355409e5e57c525a108f1c2e156abb005b90a6/scikit_learn-1.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Path to dependency file: /Day2/Creating DS ENV/requirements.txt
Path to vulnerable library: /Day2/Creating DS ENV/requirements.txt
Dependency Hierarchy: - :x: **scikit_learn-1.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl** (Vulnerable Library)
Found in HEAD commit: 5830f411b03412c02b023ec7b8edb5c1934d18b0
Found in base branch: main
### Vulnerability DetailsA 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 FixType: 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](https://www.whitesourcesoftware.com/full_solution_bolt_github)