CVE
CVE-2024-5206
- First Reported
- Oct 20, 2026
- Latest Reported
- Oct 20, 2026
CVE Enrichment
- CVSS
- 4.7 MEDIUM
- EPSS
- 0.19%
- EPSS Percentile
- 7.99%
- CISA KEV
- Not listed
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.
Technical Details
- CVSS Version
- 3.1
- CNA / NVD Source
- security@huntr.dev
- Published
- 07-Jun-2024 02:16
- Last Modified
- 17-Jun-2026 15:15
- EPSS Date
- 2026-10-20
- CWE
- CWE-921, CWE-922
CVSS Vector
CVSS:3.1 AV:L /
AC:H /
PR:L /
UI:N /
S:U /
C:H /
I:N /
A:N
Affected Products / Versions
- scikit-learn / scikit-learn
Version / affected range: * · before (exclusive) 1.5.0
cpe:2.3:a:scikit-learn:scikit-learn:*:*:*:*:*:python:*:*
References
Sources: NVD · FIRST EPSS · CISA KEV
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Reported Context (1)
- | 3 | CVE-2024-5206 | scikit-learn | 32.9% | 5.3 | MEDIUM | Orca’s 2026 AI Security Report Finds Widespread Unpatched Vulnerabilities and Exposed Credentials
CVE (3)
Malware (1)
People (2)
Threat Actors (1)
MITRE ATT&CK (4)
Vendors (7)
Products (10)
Tools (3)
Note: Related entities, including threat actors, malware, CVEs, MITRE ATT&CK techniques, vendors, products, tools, countries, and industries, are shown when they appear in the same reporting. Their presence does not necessarily mean they were targeted, compromised, vulnerable, responsible for the activity, or directly involved in the incident.