Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2025-49837 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Rvc-Boss Gpt-Sovits-Webui. Its CVSS base score is 8.9 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 48th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-49837 is an unsafe deserialization vulnerability (CWE-502) affecting GPT-SoVITS-WebUI, an open-source voice conversion and text-to-speech web interface. The issue resides in the vr.py module's AudioPre class, where the model_choose parameter accepts unsanitized user input representing a model path. This input is passed to the uvr function, which instantiates AudioPre with the path (appending a .pth extension) and loads the file using torch.load. Versions up to 20250228v3 are vulnerable, with a CVSS v3.1 base score of 9.8 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H).
A remote, unauthenticated attacker can exploit this vulnerability over the network with low complexity and no user interaction by supplying a malicious model path. The torch.load deserialization of a crafted .pth file enables arbitrary code execution on the server, potentially leading to full system compromise with high impacts on confidentiality, integrity, and availability.
The GitHub Security Lab advisory (GHSL-2025-049_GHSL-2025-053) details the flaw with code references in vr.py and webui.py but confirms no patched versions were available at publication on 2025-07-15. Mitigation requires avoiding untrusted model paths and validating/sanitizing inputs before torch.load; users should monitor the GPT-SoVITS repository for fixes.
This vulnerability is notable in AI/ML contexts, as GPT-SoVITS leverages PyTorch for model handling in voice synthesis pipelines, highlighting deserialization risks in ML web UIs. No public exploitation in the wild is reported.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-21561
Vulnerability Data
GPT-SoVITS-WebUI is a voice conversion and text-to-speech webUI. In versions 20250228v3 and prior, there is an unsafe deserialization vulnerability in vr.py AudioPre. The model_choose variable takes user input (e.g. a path to a model) and passes it to the uvr…
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function. In uvr, a new instance of AudioPre class is created with the model_path attribute containing the aforementioned user input (here called locally model_name). Note that in this step the .pth extension is added to the path. In the AudioPre class, the user input, here called model_path, is used to load the model on that path with torch.load, which can lead to unsafe deserialization. At time of publication, no known patched versions are available.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: gpt
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.