CVE-2026-31219
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 · uneditedThe _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) is vulnerable to insecure deserialization (CWE-502). When a user provides a single model file path (e.g., .pt or .pth) via the --model command-line argument, the function loads the file using torch.load() without enabling the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects through the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution during deserialization on the victim's system.
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 confidenceThe _load_model() function in neural_magic_training.py loads PyTorch model files (.pt/.pth) via torch.load() without the weights_only=True parameter, allowing arbitrary Python object deserialization through Pickle. Attackers can provide malicious model files via the --model CLI argument to achieve arbitrary code execution on the victim's system during deserialization.
Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.
CVSS 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
- Low
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- High
CVSS:3.1/AV:N/AC:L/PR:L/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 the vulnerable fileSearch for neural_magic_training.py in your codebase or installed packages. Use: find / -name 'neural_magic_training.py' 2>/dev/null or grep -r 'neural_magic_training' .Affected if The file exists in your environment and contains the _load_model() function
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Inspect the _load_model() functionOpen neural_magic_training.py and locate the _load_model() function definition. Examine the torch.load() call within it.Affected if The function contains a torch.load() call that does not include weights_only=True as a parameter
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Check CLI argument handlingSearch for the --model argument handling in the file. Look for argparse or argument parsing code that processes model file paths.Affected if The application accepts model file paths via CLI and passes them to _load_model()
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Verify model file extension handlingCheck if the code accepts .pt or .pth file extensions as valid model inputs.Affected if The application accepts .pt/.pth files and loads them with the vulnerable torch.load() call
Your environment is affected if neural_magic_training.py exists with _load_model() that uses torch.load() without weights_only=True and accepts model files via CLI.
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 dataAdd weights_only=True to all torch.load() calls to restrict deserialization to safe tensor types only, or implement model file integrity validation and signing to ensure only trusted models are loaded.
- Consultation2.0 h
- Implementation1.0 h
- Testing2.0 h
- Review / QA1.0 h
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Free · runs locallyCheck whether your project pulls in CVE-2026-31219 — 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-2026-31219 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
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- Version or environment caveats, and links to real fixes
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