CVE-2025-49839
Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.
NVD · uneditedGPT-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. 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.
Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.
dbcve analysis · high confidenceIn GPT-SoVITS-WebUI versions 20250228v3 and prior, the bsroformer.py module accepts user-supplied model paths through the model_choose variable and passes them to the uvr function, which instantiates Roformer_Loader with this input. The Roformer_Loader class then uses torch.load() on the user-controlled path (with .ckpt extension added), enabling unsafe deserialization where a malicious pickle file can execute arbitrary code.
Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.
Affected products & versions What the vendor confirmedThe version ranges the vendor confirmed as vulnerable. If your version sits inside a range here, treat yourself as exposed until you have upgraded.
NVD · CPE data<= 20250228v3CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.
From the vector- Attack vector
- Network
- Complexity
- Low
- Privileges
- None
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- High
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.
dbcve checksWork through these to decide whether this CVE applies to you.
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Locate GPT-SoVITS-WebUI installationSearch for the GPT-SoVITS-WebUI directory or package in common locations such as the current working directory, Python site-packages, or typical clone paths like ~/GPT-SoVITS-WebUIAffected if The software is present on the system
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Identify installed versionCheck for a version file, git tag, or metadata in the installation directory; common locations include a version.txt, __version__ in __init__.py, or the git log showing the latest commit dateAffected if The version is 20250228v3 or any version prior to it
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Verify vulnerable module existsLocate the bsroformer.py file within the codebase and inspect it for the Roformer_Loader class usage with torch.load() on user-supplied pathsAffected if The file exists and contains torch.load() calls without weights_only=True on paths derived from user input (model_choose variable)
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Check if UVR/Roformer feature is accessibleIdentify whether the web interface or API exposes the uvr function or Roformer model loading capability that accepts custom model pathsAffected if Users can supply arbitrary model paths to the UVR or Roformer feature
A user is affected if GPT-SoVITS-WebUI version 20250228v3 or earlier is installed AND the bsroformer module with its unsafe torch.load() is accessible via the application interface.
Generated from the published advisory. Verify against your own configuration.
Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.
From vendor dataReplace torch.load() with torch.load(..., weights_only=True) or implement a custom secure unpickler that validates the model file before loading. Add model path validation and allowlisting to prevent loading from arbitrary locations.
- Consultation3.0 h
- Implementation6.0 h
- Testing4.0 h
- Review / QA2.0 h
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Free · runs locallyCheck whether your project pulls in CVE-2025-49839 — or any other known-vulnerable package — straight from your lock files. Free and open source; it runs locally and uploads nothing.
References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.
Primary sourcesPractitioner notes
ContributedPeer-ranked notes from engineers who’ve handled CVE-2025-49839 in production — separate from our analysis above.
The advisory tells you what broke. It rarely tells you what actually worked. If you’ve dealt with this one, that detail is what the next engineer is searching for.
- The version that genuinely resolved it — not the one the vendor claimed
- A config change or rule that shut the vector down
- A gotcha in the upgrade path that cost you an afternoon
No notes yet
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- Version or environment caveats, and links to real fixes
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