CVE-2026-31218
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 loading a model state dictionary from a state_dict.pt file via torch.load(), the function does not enable 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 state_dict.pt file within a directory specified via the --model argument, leading to arbitrary code execution during the deserialization process 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 uses torch.load() to deserialize model state from state_dict.pt without the weights_only=True parameter, allowing arbitrary Pickle object deserialization. An attacker can provide a malicious state_dict.pt file via the --model argument to achieve arbitrary code execution during model loading.
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 scriptSearch for neural_magic_training.py in your environment using 'find . -name neural_magic_training.py' or 'locate neural_magic_training.py'Affected if The file exists in your environment
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Identify the _load_model functionOpen neural_magic_training.py and locate the _load_model() function definition using grep or a text editorAffected if The _load_model() function exists in the file
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Inspect torch.load() usageWithin the _load_model function, search for torch.load( calls and examine the parameters passedAffected if torch.load() is called without weights_only=True parameter
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Check for --model argument handlingLook for command-line argument parsing related to --model and verify it passes user-controlled input to torch.load()Affected if The --model argument directly provides the file loaded by torch.load()
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Verify pickle deserialization riskConfirm that the loaded file (state_dict.pt or similar) can be user-supplied and is deserialized via torch.load() without weights_only=TrueAffected if External pickle files can be loaded without the weights_only=True restriction
Your environment is affected if neural_magic_training.py contains a _load_model() function that uses torch.load() to load user-supplied model files without the weights_only=True parameter.
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 parameter to torch.load() call in _load_model() function to restrict deserialization to only safe tensor objects, preventing arbitrary code execution via malicious pickle payloads.
- Consultation1.0 h
- Implementation1.0 h
- Testing2.0 h
- Review / QA1.0 h
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Free · runs locallyCheck whether your project pulls in CVE-2026-31218 — 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-2026-31218 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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