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CRITICAL severity

CVE-2020-13092 — scikit-learn

CRITICAL

CVE-2020-13092 is a critical-severity (CVSS 9.8) Deserialization of Untrusted Data vulnerability in scikit-learn. 1 public exploit or proof-of-concept reference is indexed, so weaponization risk is real. The records list no fixed version yet; check the vendor advisory below and apply the mitigations in the meantime.

scikit-learn Deserialization of Untrusted Data

Also known asGHSA-jjw5-xxj6-pcv5PYSEC-2020-107
Published
Updated
Affected
1 pkg
Patched
See advisory
Exploits
1 known
Exploitation data as of Oct 28, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
3.4%probability of exploitation in next 30 days
Lower Risk+0.04%
Lower risk than most CVEs89th percentile — riskier than 89% of all scored CVEsHighest risk
0.00%1.42%2.83%4.25%0.6%3.4%Apr 26Aug 26Oct 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

Where this sits among everything scored

Real-World Exposure

1 pkg affected
🐍scikit-learn

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

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

Affected Packages

1 total
EcosystemPackageVulnerable rangeFix
🐍PyPIscikit-learnall versionsNo fix

Affected Products

1 product · 1 configurations
Application
scikit-learnscikit-learn
≤ 0.23.0
range
Exploits & PoCs
1

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

scikit-learn (aka sklearn) through 0.23.0 can unserialize and execute co…

github.comNVDMay 2020

Detection & mitigation playbook

Open-source dependency

  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for scikit-learn, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Remediation status

    The records we use list no patched version of scikit-learn for CVE-2020-13092. Check the maintainer's advisory, and where your build allows, override or pin the dependency away from the vulnerable range.

  3. Mitigate without a patch

    Do not deserialise data from untrusted sources: where the format allows it, restrict deserialisation to an explicit allowlist of expected types, and prefer a data-only format (JSON, Protobuf) over one that can reconstruct arbitrary objects until you can upgrade.

Frequently Asked Questions

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
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CVE-2020-13092: scikit-learn (Critical 9.8)