CriticalVulnerability

CVE-2020-13092: scikit-learn unsafe deserialization via joblib.load of untrusted file

Source: NVD(opens in a new tab)
Identifier: CVE-2020-13092
Published: May 15, 2020
Record updated: October 10, 2026

Affected: scikit-learn <= 0.23.0
Fixed in: No fixed version was stated when the source was last read.
Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: 3.4%

Summary

scikit-learn (aka sklearn) through 0.23.0 can unserialize and execute commands from an untrusted file passed to joblib.load(), if __reduce__ makes an os.system call. Third parties dispute this issue, noting that joblib.load() is documented as unsafe and that using it securely is the user's responsibility. The weakness is classified as CWE-502, Deserialization of Untrusted Data.

Mitigation

The source does not state a fix yet. Check the original advisory for updates.

Details

Scores and exploitation

CVSS score: 9.8(critical)
EPSS: 3.4%Estimated probability that this CVE is exploited in the next 30 days, from the FIRST Exploit Prediction Scoring System. Read on November 4, 2026.

Weaknesses and techniques

Weakness (CWE): CWE-502
Attack pattern (CAPEC): CAPEC-586
AI threat technique (MITRE ATLAS): AML.T0010

Affected

Packages: scikit-learn@<= 0.23.0

Classification

Attack type: Supply chain
Attack sophistication: Trivial
AI component targeted: Framework
Impact: Confidentiality,
Integrity,
Availability
Labels: security

How this record was produced

Summaries, mitigations and labels on this page are generated automatically from the source. The linked source is authoritative. The source gives this advisory a description and no title, so the title above is a headline generated from the source text.

Original source: https://nvd.nist.gov/vuln/detail/CVE-2020-13092

First tracked: February 15, 2026 at 08:42 PM ET, from NVD/CVE Database

Classifier: claude-haiku-5-5, prompt v4

Classifier confidence: 85% (self-assessed)

Affected packages in the Exposure Registry

Matched by package name and ecosystem. Each entry shows whether the package delegates to a language model and how many tracked packages depend on it.

scikit-learnPyPINo LLM dependency detected · 74 tracked dependents

Summary

Mitigation

Affected packages in the Exposure Registry

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