CVE-2020-28975: High severity scikit-learn vulnerability

Published Nov 21, 2020
·
Updated

DISPUTED svmpredictvalues 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 nsupport 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.

Affected Software

2 affected componentsFixes available
pip/scikit-learn>=0.23.2<1.0.1
1.0.1
scikit-learn scikit-learn>=0.23.2<1.0.1

Event History

Nov 21, 2020
CVE Published
via MITRE·12:00 AM
Data Sourced
via MITRE·12:00 AM
Description
Disputed
09:15 PM
May 24, 2022
Advisory Published
via GitHub·05:34 PM
Free Weekly Intel

Don't miss critical vulnerabilities

Join thousands of security professionals who receive our weekly digest of trending CVEs, zero-days, and exploited vulnerabilities.

No spam. Unsubscribe anytime.

Frequently Asked Questions

1

What is the severity of CVE-2020-28975?

CVE-2020-28975 has been reported as a denial of service vulnerability that can lead to a segmentation fault.

2

How do I fix CVE-2020-28975?

To fix CVE-2020-28975, upgrade scikit-learn to version 1.0.1 or later.

3

Which software is affected by CVE-2020-28975?

CVE-2020-28975 affects scikit-learn versions between 0.23.2 and 1.0.1.

4

What does CVE-2020-28975 impact?

CVE-2020-28975 impacts the svm_predict_values function in the Libsvm implementation.

5

Can CVE-2020-28975 be exploited remotely?

Yes, CVE-2020-28975 can be exploited remotely through crafted model inputs.

Contact

SecAlerts Pty Ltd.
132 Wickham Terrace
Fortitude Valley,
QLD 4006, Australia
info@secalerts.co
By using SecAlerts services, you agree to our services end-user license agreement. This website is safeguarded by reCAPTCHA and governed by the Google Privacy Policy and Terms of Service. All names, logos, and brands of products are owned by their respective owners, and any usage of these names, logos, and brands for identification purposes only does not imply endorsement. If you possess any content that requires removal, please get in touch with us.
© 2026 SecAlerts Pty Ltd.
ABN: 70 645 966 203, ACN: 645 966 203