CVE-2025-5148
Published: 25 May 2025
Summary
CVE-2025-5148 is a medium-severity Improper Input Validation (CWE-20) vulnerability. Its CVSS base score is 4.8 (Medium).
Operationally, ranked at the 43.2th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-16239
Vulnerability details
A vulnerability was found in FunAudioLLM InspireMusic up to bf32364bcb0d136497ca69f9db622e9216b029dd. It has been classified as critical. Affected is the function load_state_dict of the file inspiremusic/cli/model.py of the component Pickle Data Handler. The manipulation leads to deserialization. An attack has to…
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be approached locally. This product is using a rolling release to provide continious delivery. Therefore, no version details for affected nor updated releases are available. The name of the patch is 784cbf8dde2cf1456ff808aeba23177e1810e7a9. It is recommended to apply a patch to fix this issue.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
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 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.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
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.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.