CVE-2025-43846
Published: 05 May 2025
Summary
CVE-2025-43846 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Rvc-Project Retrieval-Based-Voice-Conversion-Webui. Its CVSS base score is 8.9 (High).
Operationally, ranked in the top 12.9% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
Deeper analysis
Retrieval-based-Voice-Conversion-WebUI versions 2.2.231006 and earlier contain an unsafe deserialization flaw in the voice conversion framework built on VITS. The ckpt_path1 variable accepts an attacker-controlled path that is passed directly to the show_info function in process_ckpt.py, which then invokes torch.load on the supplied file without any validation or safe loading mechanism.
An unauthenticated remote attacker can supply a malicious checkpoint file over the network and trigger arbitrary code execution on the server. The CVSS 8.9 rating reflects the absence of required authentication, user interaction, or special conditions for successful exploitation.
No patches were available at the time of disclosure. The referenced GitHub Security Lab advisory (GHSL-2025-012) and the linked source files in infer-web.py and process_ckpt.py document the call path and confirm the lack of mitigations. The associated EPSS score rose from a low baseline to a peak of 0.0602 before receding, indicating measurable post-disclosure exploitation interest in this machine-learning application.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-13508
Vulnerability details
Retrieval-based-Voice-Conversion-WebUI is a voice changing framework based on VITS. Versions 2.2.231006 and prior are vulnerable to unsafe deserialization. The ckpt_path1 variable takes user input (e.g. a path to a model) and passes it to the show_info function in process_ckpt.py, which…
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uses it to load the model on that path with torch.load, which can lead to unsafe deserialization and remote code execution. As of time of publication, no known patches exist.
- 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.
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.