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-2025-49841 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 47th 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-2025-49841 is an unsafe deserialization vulnerability (CWE-502) in GPT-SoVITS-WebUI, an open-source web interface for voice conversion and text-to-speech functionality. The flaw affects versions 20250228v3 and prior, specifically in the process_ckpt.py component. User input from the SoVITS_dropdown variable is passed directly to the load_sovits_new function, which uses the controlled sovits_path parameter to invoke torch.load for model loading, enabling arbitrary deserialization of untrusted data.
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), the vulnerability is exploitable remotely by unauthenticated attackers with low complexity and no user interaction required. An attacker can supply a malicious model path via the web interface, triggering deserialization during torch.load execution. This can lead to high-impact compromise of confidentiality, integrity, and availability, such as remote code execution on the hosting server.
The GitHub Security Lab advisory (GHSL-2025-049_GHSL-2025-053) identifies the issue with code references in inference_webui.py (lines 873 and 926) and process_ckpt.py (lines 100-106). No patched versions were available at the time of publication on 2025-07-15.
This vulnerability underscores risks in AI/ML web UIs that load PyTorch models from user-supplied paths, with no reported real-world exploitation at publication.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-21557
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 process_ckpt.py. The SoVITS_dropdown variable takes user input and passes it to the load_sovits_new function in process_ckpt.py. In load_sovits_new, the user…
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input, here sovits_path is used to load a model with torch.load, leading 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.