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CVE-2024-5206
June 06, 2024
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 Packages
scikit-learn (PYTHON):
Affected version(s) >=0.9 <1.5.0
Fix Suggestion:
Update to version 1.5.0
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CVSS v4
Base Score:
5.7
Attack Vector
LOCAL
Attack Complexity
HIGH
Attack Requirements
NONE
Privileges Required
LOW
User Interaction
NONE
Vulnerable System Confidentiality
HIGH
Vulnerable System Integrity
NONE
Vulnerable System Availability
NONE
Subsequent System Confidentiality
NONE
Subsequent System Integrity
NONE
Subsequent System Availability
NONE
CVSS v3
Base Score:
4.7
Attack Vector
LOCAL
Attack Complexity
HIGH
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality
HIGH
Integrity
NONE
Availability
NONE
Weakness Type (CWE)
Storage of Sensitive Data in a Mechanism without Access Control
Insecure Storage of Sensitive Information
EPSS
Base Score:
0.04