CVE-2025-54886: skops · Deserialization of Untrusted Data
Affects skopsSource CVE Database V5
Not known to be exploited · CVSS 8.4 High : upgrade to 0.13.0 soon.
Data as of 26 Oct 2026, 14:05 UTC
- EPSS
- 0.2% chance of exploitation in 30 days – higher than 12% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
- Patch
- Available – fixed in 0.13.0
CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HExploitation evidence
Disclosed: evidence found.
CVE published 8 Aug 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by 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 0.13.0.
Affected software
| Product | Affected | Fixed in |
|---|---|---|
| skops-dev skops | Affected
| Fixed in
|
AI-suggested
European organizations should immediately upgrade all instances of skops to version 0.13.0 or later to eliminate the unsafe fallback to joblib. Until upgrades are complete, organizations must implement strict validation and integrity checks on all model files before loading, including cryptographic signatures or checksums to ensure authenticity. Restrict model loading to trusted sources only and avoid loading models from unverified or external inputs. Employ runtime application self-protection (RASP) or endpoint detection and response (EDR) tools to monitor for suspicious process behaviors indicative of arbitrary code execution. Incorporate network segmentation and least privilege principles to limit the impact of potential exploitation. Additionally, conduct thorough code reviews and penetration testing focused on ML model loading components. Educate data scientists and ML engineers about the risks of deserializing untrusted data and enforce secure development lifecycle practices around model serialization and deployment. Finally, maintain up-to-date inventories of ML libraries and dependencies to rapidly respond to emerging vulnerabilities.
Description
skops is a Python library which helps users share and ship their scikit-learn based models. In versions 0.12.0 and below, the Card.get_model does not contain any logic to prevent arbitrary code execution. The Card.get_model function supports both joblib and skops for model loading. When loading .skops models, it uses skops' secure loading with trusted type validation, raising errors for untrusted types unless explicitly allowed. However, when non-.zip file formats are provided, the function silently falls back to joblib without warning. Unlike skops, joblib allows arbitrary code execution during loading, bypassing security measures and potentially enabling malicious code execution. This issue is fixed in version 0.13.0.