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-2025-49839 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.
GPT-SoVITS-WebUI, a web interface for voice conversion and text-to-speech functionality, contains an unsafe deserialization vulnerability (CWE-502) in versions 20250228v3 and prior. The issue resides in the bsroformer.py component, where the model_choose parameter accepts unsanitized user input, such as a model path, which is passed to the uvr function. This input is then used to instantiate a Roformer_Loader class, appending a .ckpt extension before loading the file via torch.load. This direct use of user-controlled data in torch.load enables arbitrary deserialization of PyTorch checkpoints, 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).
Remote, unauthenticated attackers can exploit this vulnerability over the network with low complexity and no user interaction required. By supplying a malicious model path via the model_choose parameter, an attacker triggers deserialization of a crafted .ckpt file during model loading in Roformer_Loader. Successful exploitation allows arbitrary code execution on the server, potentially granting high-impact confidentiality, integrity, and availability compromises, such as remote code execution leading to full system takeover.
GitHub Security Lab advisories (GHSL-2025-049 and GHSL-2025-053) detail the flaw with references to specific code lines in bsroformer.py and webui.py, confirming the deserialization path. At the time of publication on 2025-07-15, no patched versions were available, leaving deployments reliant on input validation, network restrictions, or disabling the affected uvr functionality until fixes emerge.
This vulnerability is particularly relevant to AI/ML practitioners deploying voice synthesis tools, as it targets PyTorch model loading in an open-source TTS pipeline, highlighting risks in user-supplied model paths common in ML webUIs. No public evidence of real-world exploitation was reported at disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-21559
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 bsroformer.py. The model_choose variable takes user input (e.g. a path to a model) and passes it to the uvr function.…
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In uvr, a new instance of Roformer_Loader class is created with the model_path attribute containing the aformentioned user input (here called locally model_name). Note that in this step the .ckpt extension is added to the path. In the Roformer_Loader 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.