What is the CVSS score of CVE-2020-13092?

CVE-2020-13092 has a CVSS 3.1 base score of 9.8 (Critical). CVSS vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H. EPSS exploitation probability: 2.6%.

Is there a public PoC exploit available for CVE-2020-13092?

Yes, a public proof-of-concept exploit is available for CVE-2020-13092. Prioritise patching immediately. EPSS: 2.6%.

Is there a patch available for CVE-2020-13092?

Yes, a patch is available for CVE-2020-13092. Update the affected software to the latest version immediately.

Scikit Learn CVE-2020-13092

CRITICAL

Deserialization of Untrusted Data (CWE-502)

2020-05-15 cve@mitre.org

Deserialization Scikit Learn

9.8

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

9.8 CRITICAL

AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Attack Vector

Network

Attack Complexity

Low

Privileges Required

None

User Interaction

None

Scope

Unchanged

Confidentiality

High

Integrity

High

Availability

High

Lifecycle Timeline

CVE Published

May 15, 2020 - 19:15 nvd

CRITICAL 9.8

Blast Radius

ecosystem impact

† from your stack dependencies † transitive graph · vuln.today resolves 4-path depth

20 pypi packages depend on scikit-learn (18 direct, 2 indirect)

Ecosystem-wide dependent count for version 0.23.0.

DescriptionNVD

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

AnalysisAI

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. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.

Technical ContextAI

This vulnerability is classified as Deserialization of Untrusted Data (CWE-502), which allows attackers to execute arbitrary code through malicious serialized objects. 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 products include: Scikit-Learn. Version information: through 0.23.0.

RemediationAI

No vendor patch is available at time of analysis. Monitor vendor advisories for updates. Avoid deserializing untrusted data. Use safe serialization formats (JSON). Implement integrity checks and type allowlists.