Focus on scikit-learn vulnerabilities and metrics.
Last updated: 08 Mar 2025, 23:25 UTC
This page consolidates all known Common Vulnerabilities and Exposures (CVEs) associated with scikit-learn. We track both calendar-based metrics (using fixed periods) and rolling metrics (using gliding windows) to give you a comprehensive view of security trends and risk evolution. Use these insights to assess risk and plan your patching strategy.
For a broader perspective on cybersecurity threats, explore the comprehensive list of CVEs by vendor and product. Stay updated on critical vulnerabilities affecting major software and hardware providers.
Total scikit-learn CVEs: 3
Earliest CVE date: 15 May 2020, 19:15 UTC
Latest CVE date: 06 Jun 2024, 19:16 UTC
Latest CVE reference: CVE-2024-5206
30-day Count (Rolling): 0
365-day Count (Rolling): 1
Calendar-based Variation
Calendar-based Variation compares a fixed calendar period (e.g., this month versus the same month last year), while Rolling Growth Rate uses a continuous window (e.g., last 30 days versus the previous 30 days) to capture trends independent of calendar boundaries.
Month Variation (Calendar): 0%
Year Variation (Calendar): 0%
Month Growth Rate (30-day Rolling): 0.0%
Year Growth Rate (365-day Rolling): 0.0%
Average CVSS: 4.17
Max CVSS: 7.5
Critical CVEs (≥9): 0
| Range | Count |
|---|---|
| 0.0-3.9 | 1 |
| 4.0-6.9 | 1 |
| 7.0-8.9 | 1 |
| 9.0-10.0 | 0 |
These are the five CVEs with the highest CVSS scores for scikit-learn, sorted by severity first and recency.
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.
svm_predict_values in svm.cpp in Libsvm v324, as used in scikit-learn 0.23.2 and other products, allows attackers to cause a denial of service (segmentation fault) via a crafted model SVM (introduced via pickle, json, or any other model permanence standard) with a large value in the _n_support array. NOTE: the scikit-learn vendor's position is that the behavior can only occur if the library's API is violated by an application that changes a private attribute.
scikit-learn (aka sklearn) through 0.23.0 can unserialize and execute commands from an untrusted file that is passed to the joblib.load() function, if __reduce__ makes an os.system call. NOTE: third parties dispute this issue because the joblib.load() function is documented as unsafe and it is the user's responsibility to use the function in a secure manner