CVE-2025-71340
Published: 25 June 2026
Summary
CVE-2025-71340 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability. Its CVSS base score is 7.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Compromise Software Supply Chain (T1195.002); ranked at the 21.8th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-210344
Vulnerability details
picklescan through 0.0.26 fails to detect malicious pickle files that invoke idlelib.pyshell.ModifiedInterpreter.runcode in __reduce__ methods. Attackers can embed undetected code in pickle files that executes arbitrary commands when the file is loaded via pickle.load(), enabling supply chain attacks on PyTorch…
more
models and saved Python objects. This is fixed in version 0.0.30.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Directly enables supply chain compromise via undetected malicious pickle deserialization leading to Python RCE on model/object load.
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.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Evaluation of untrusted data handling (deserialization testing) reveals unsafe processing, which the required remediation process addresses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Validates or rejects untrusted serialized data before deserialization occurs.
Identifies and blocks malicious code introduced through deserialization of untrusted data at system boundaries.
Integrity verification of serialized information can detect tampering before deserialization occurs.
Provenance of associated data allows detection of untrusted sources before deserialization or processing occurs.