CVE-2025-43851
Published: 05 May 2025
Summary
CVE-2025-43851 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 prior contain an unsafe deserialization flaw in the voice conversion framework based on VITS. User-controlled input supplied via the model_choose variable is passed to the uvr function in vr.py, which instantiates an AudioPre object whose model_path attribute is then loaded directly with torch.load, enabling arbitrary code execution.
An unauthenticated remote attacker can exploit the issue over the network by supplying a malicious model path, achieving full remote code execution with impacts on confidentiality, integrity, and availability as reflected in the CVSS 8.9 rating and CWE-502 classification.
The referenced GitHub Security Lab advisory GHSL-2025-012_GHSL-2025-022 describes the vulnerability in detail; at the time of publication no patches were known to exist.
The EPSS score rose from lower values to a peak of 0.0602 on 2026-03-01 before receding to the current 0.0311, indicating that exploitation interest emerged after disclosure.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-13503
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 model_choose variable takes user input (e.g. a path to a model) and passes it to the uvr function in vr.py. In…
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uvr , a new instance of AudioPre class is created with the model_path attribute containing the aformentioned user input. In the AudioPre class, the user input, is used 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.