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VulnerabilityPublished Updated

CVE-2025-54413: skops · Insufficient Type Distinction

Affects skopsSource CVE Database V5

0

Not known to be exploited · CVSS 8.7 High : upgrade to 12.0.0 soon.

Data as of 26 Oct 2026, 09:15 UTC

CVSS 4.0 base score
8.7High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.1% chance of exploitation in 30 days – higher than 4% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 12.0.0
CVSS:4.0/AV:L/AC:L/AT:P/PR:N/UI:A/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 26 Jul 2025

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.

What to do

Upgrade skops to 12.0.0.

Affected software

ProductAffectedFixed in
PyPIskopspkg:pypi/skopsOther threatsAffected
  • <12.0.0
Fixed in
  • 12.0.0

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

European organizations should take immediate and specific actions beyond generic patching advice: 1) Upgrade all instances of skops to version 12.0.0 or later to eliminate the vulnerability. 2) Implement strict validation and integrity checks on all machine learning models before loading, including cryptographic signatures or hashes to ensure models originate from trusted sources. 3) Restrict the ability to load models to trusted users and environments, employing role-based access controls and least privilege principles. 4) Monitor and log all model loading activities to detect anomalous behavior indicative of exploitation attempts. 5) Educate data scientists and ML engineers about the risks of loading untrusted models and enforce policies preventing the use of models from unknown or unverified sources. 6) Consider sandboxing or isolating the environment where models are loaded to limit the impact of potential code execution. 7) Review and update incident response plans to include scenarios involving ML model compromise.

Description

skops is a Python library which helps users share and ship their scikit-learn based models. Versions 0.11.0 and below contain an inconsistency in MethodNode, which can be exploited to access unexpected object fields through dot notation. This can be used to achieve arbitrary code execution at load time. While this issue may seem similar to GHSA-m7f4-hrc6-fwg3, it is actually more severe, as it relies on fewer assumptions about trusted types. This is fixed in version 12.0.0.

References

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