How severe is CVE-2020-28975?

CVE-2020-28975 has a CVSS 3.x base score of 7.5, rated high severity. It is exploitable over network with low attack complexity, requires no privileges and no user interaction. Impact on confidentiality is none, integrity none, and availability high.

Is CVE-2020-28975 being actively exploited?

It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 3% (88th percentile), an estimate of the probability of exploitation in the next 30 days.

What products are affected by CVE-2020-28975?

CVE-2020-28975 affects Scikit-learn. See the affected-products list for the exact vulnerable versions.

How do I fix CVE-2020-28975?

Review the linked vendor and NVD advisories for patched versions and mitigations, then upgrade or apply the recommended workaround. Given its high severity, prioritise patching exposed systems.

When was CVE-2020-28975 published?

CVE-2020-28975 was published on 2020-11-21 and last updated on 2026-06-17.

CVE-2020-28975

CVE-2020-28975 is a high-severity vulnerability in Scikit-learn with a CVSS 3.x base score of 7.5. It is not currently listed as actively exploited by CISA, and its EPSS exploit-prediction score is low.

Key facts

Severity: High (CVSS 3.x base score 7.5)
CVSS v2: 5.0
EPSS exploit prediction: 3% (88th percentile)
Actively exploited: Not listed in CISA KEV
Affected product: Scikit-learn
Published: 2020-11-21
Last modified: 2026-06-17

Description

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.

Frequently asked questions

What is CVE-2020-28975?: 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.
How severe is CVE-2020-28975?: CVE-2020-28975 has a CVSS 3.x base score of 7.5, rated high severity. It is exploitable over network with low attack complexity, requires no privileges and no user interaction. Impact on confidentiality is none, integrity none, and availability high.
Is CVE-2020-28975 being actively exploited?: It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 3% (88th percentile), an estimate of the probability of exploitation in the next 30 days.
What products are affected by CVE-2020-28975?: CVE-2020-28975 affects Scikit-learn. See the affected-products list for the exact vulnerable versions.
How do I fix CVE-2020-28975?: Review the linked vendor and NVD advisories for patched versions and mitigations, then upgrade or apply the recommended workaround. Given its high severity, prioritise patching exposed systems.
When was CVE-2020-28975 published?: CVE-2020-28975 was published on 2020-11-21 and last updated on 2026-06-17.

References

Affected products (1)

cpe:2.3:a:scikit-learn:scikit-learn:*:*:*:*:*:*:*:*

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