Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2025-54886 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability. Its CVSS base score is 8.4 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 12th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Machine Learning Libraries; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
skops is a Python library for sharing scikit-learn models. In versions 0.12.0 and earlier, the Card.get_model function lacks safeguards against arbitrary code execution. It attempts secure loading with trusted-type validation for .skops files but silently falls back to joblib when presented with any other file format, allowing deserialization of untrusted data without warning or restriction.
An attacker with the ability to supply a model file to Card.get_model can achieve remote code execution by providing a joblib-formatted artifact containing malicious payloads. No user privileges or interaction are required, and the CVSS 8.4 rating reflects the resulting high impact on confidentiality, integrity, and availability through execution of attacker-controlled code during the load operation.
The vulnerability is addressed in version 0.13.0. The accompanying GitHub security advisory GHSA-378x-6p4f-8jgm and the referenced commit detail the removal of the unsafe fallback path and the enforcement of skops-format handling.
EPSS remains low and unchanged at 0.0134 with no observed rise after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-23967
Vulnerability Data
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…
more
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.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Machine Learning Libraries
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: scikit-learn
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.