CVE-2024-5206

MEDIUMCVSS 4.7/10EPSS 0.19%

Last modified

CVE-2024-5206 is a medium-severity vulnerability rated 4.7/10 on the CVSS scale. 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. EPSS estimates a 0.19% chance of exploitation in the next 30 days.

Description

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.

Metrics

CVSS 3.1
4.7/10

CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N

EPSS Probability
0.19%

8.5th percentile

Probability of exploitation in the next 30 days. Learn more

Weakness Enumeration

Affected Software

VendorProductVersions
Scikit-LearnScikit-Learn< 1.5.0

References

Timeline

Published
Last Modified
Status
Modified

Frequently Asked Questions

What is CVE-2024-5206?
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.
How severe is CVE-2024-5206?
CVE-2024-5206 has a CVSS score of 4.7/10 (MEDIUM severity). The EPSS model estimates a 0.19% probability of exploitation in the next 30 days.
How do I fix CVE-2024-5206?
Check the vendor references and advisories linked above for patched versions and mitigation guidance. You can also run a Strix scan to test if your systems are affected.

Are you affected by CVE-2024-5206?

Run a free Strix scan to check your systems for this vulnerability.

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Source: NVD / NIST