Scikit Learn
CVE-2024-5206
MEDIUM
Vulnerability Assessment
| Weakness |
CWE
|
| Impact | CVSS score of 4.7 (MEDIUM). Attack characteristics: local attack vector, high complexity, low privileges required, no user interaction not required. |
| Exploit ScenarioDerived | Successful exploitation could compromise the medium-severity affected system. |
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Alert meAnalysisAI
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. Rated medium severity (CVSS 4.7).
RemediationAI
A vendor patch is available. Apply the latest security update as soon as possible. Apply vendor patches when available. Implement network segmentation and monitoring as interim mitigations.
Affected Packages
Source: OSV (Open Source Vulnerabilities)
Affects your stack
Affects 1 package in pypi
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Technical ContextAI · show details
This vulnerability is classified under CWE-921. 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. Affected products include: Scikit-Learn. Version information: version 1.5.0..
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Severity by source
AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N
Primary rating from NVD · only source for this CVE.
CVSS VectorNVD
DescriptionNVD
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.
Threat Intelligence Overview
NVD
CVE.org
EPSS
GitHub
How this decision was reached
The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.
What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
Information disclosure occurs when an application unintentionally exposes sensitive data that aids attackers in reconnaissance or directly compromises security.
Affected Products (CPE)NVD
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