CVE-2026-31239
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 mamba language model framework thru 2.2.6 is vulnerable to insecure deserialization (CWE-502) when loading pre-trained models from HuggingFace Hub. The MambaLMHeadModel.from_pretrained() method uses torch.load() to load the pytorch_model.bin weight file without enabling the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by publishing a malicious model repository on HuggingFace Hub. When a victim loads a model from this repository, arbitrary code is executed on the victim's system in the context of the mamba process.
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 mamba framework through v2.2.6 uses torch.load() in MambaLMHeadModel.from_pretrained() to load model weights from HuggingFace Hub without the weights_only=True security parameter. Since torch.load() defaults to pickle deserialization, an attacker can craft a malicious pytorch_model.bin containing arbitrary serialized Python objects that execute code upon 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
- 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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Check mamba framework versionRun 'pip show mamba' or 'python -c "import mamba; print(mamba.__version__)"' to determine the installed versionAffected if Version is 2.2.6 or earlier (the framework through v2.2.6 is affected)
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Find code using MambaLMHeadModel.from_pretrained()Search your codebase for 'MambaLMHeadModel.from_pretrained' to identify if this method is being called to load modelsAffected if Code loads models using MambaLMHeadModel.from_pretrained() from the mamba framework
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Inspect torch.load() calls in MambaLMHeadModelExamine the MambaLMHeadModel.from_pretrained() source code or wrapper to see how torch.load() is invoked when loading pytorch_model.binAffected if torch.load() is called without the weights_only=True parameter
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Identify model weight sourceDetermine if the application loads model weights from HuggingFace Hub or other untrusted remote sourcesAffected if Model weights are loaded from HuggingFace Hub or other untrusted sources without prior verification
You are affected if you use mamba framework v2.2.6 or earlier and load models via MambaLMHeadModel.from_pretrained() from HuggingFace Hub without verifying that torch.load() includes weights_only=True.
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 when loading model weights from untrusted sources like HuggingFace Hub, or implement a model verification/sandboxing mechanism before deserialization.
- Consultation2.0 h
- Implementation3.0 h
- Testing3.0 h
- Review / QA2.0 h
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Free · runs locallyCheck whether your project pulls in CVE-2026-31239 — 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-2026-31239 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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