CVE-2025-43850
Published: 05 May 2025
Summary
CVE-2025-43850 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 are affected by an unsafe deserialization flaw in this VITS-based voice conversion framework. User-controlled input to the ckpt_dir variable is passed directly to the change_info function in export.py, which then calls torch.load on the supplied model path without safeguards.
An unauthenticated remote attacker can supply a crafted model path over the network to achieve remote code execution by abusing Python deserialization during the load operation. The vulnerability carries a CVSS 4.0 score of 8.9 with network attack vector, no required privileges or user interaction, and high impact on confidentiality, integrity, and availability.
The GitHub Security Lab advisory (GHSL-2025-012) confirms the code paths in infer-web.py and export.py but states that no patches existed at publication time.
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. The affected software is an AI/ML voice-conversion application.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-13502
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_dir variable takes user input (e.g. a path to a model) and passes it to the change_info function in export.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.