CVE-2025-10155
Published: 17 September 2025
Summary
CVE-2025-10155 is a critical-severity Improper Input Validation (CWE-20) vulnerability in Mmaitre314 Picklescan. Its CVSS base score is 9.3 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Masquerade File Type (T1036.008); ranked at the 22.1th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Machine Learning Libraries; in the Supply Chain and Deployment risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-29706
Vulnerability details
An Improper Input Validation vulnerability in the scanning logic of mmaitre314 picklescan versions up to and including 0.0.30 allows a remote attacker to bypass pickle files security checks by supplying a standard pickle file with a PyTorch-related file extension. When…
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the pickle file incorrectly considered safe is loaded, it can lead to the execution of malicious code.
- 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: pytorch
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The improper input validation allows masquerading malicious pickle files with PyTorch extensions to bypass security scans (T1036.008, T1211), facilitating arbitrary Python code execution upon loading (T1059.006).
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.
Directly implements checks on information inputs to reject invalid data before processing.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.