CVE-2026-24233
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 · uneditedNVIDIA TensorRT-LLM for Linux contains a vulnerability in the restricted unpickler used for model weight deserialization, where a local, unauthenticated attacker could cause deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, and information disclosure.
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 · moderate confidenceNVIDIA TensorRT-LLM for Linux contains a deserialization vulnerability in its restricted unpickler used for model weight loading. A local, unauthenticated attacker could supply untrusted serialized data (model weights) to trigger insecure deserialization, potentially achieving code execution, privilege escalation, data tampering, or information disclosure.
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
- Local
- Complexity
- Low
- Privileges
- None
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- High
CVSS:3.1/AV:L/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.
-
Verify NVIDIA TensorRT-LLM installationCheck if TensorRT-LLM is installed by looking for the tensorrt_llm package: `pip show tensorrt-llm` or `nvidia-smi` showing TensorRT capabilities, or check for /usr/local/tensorrt_llm directoriesAffected if TensorRT-LLM is installed and the version is within the affected range (any version using the vulnerable restricted unpickler)
-
Identify the model weight loading mechanismInspect code or configuration that loads model weights - look for usage of pickle, torch.load, or custom unpickler functions in the model loading pipelineAffected if The model loading code uses pickle-based deserialization (torch.load with weights_only=False, or custom unpickler) to load untrusted model weights
-
Check for untrusted model weight sourcesReview where model weights are loaded from - check if they come from untrusted network sources, user-uploaded files, or external repositories without verificationAffected if Model weights are loaded from untrusted sources without cryptographic validation or signature verification
-
Examine the restricted unpickler implementationLocate the TensorRT-LLM unpickler code (typically in weight loading utilities) and verify if it properly restricts unsafe pickle operationsAffected if The unpickler allows dangerous pickle operations or can be bypassed to load arbitrary pickle objects
-
Audit model artifact handlingInspect configuration files and runtime logs for model loading paths, looking for lack of integrity verification (checksums, signatures) on model filesAffected if Model weights are loaded without integrity verification mechanisms in place
A user is affected if they have NVIDIA TensorRT-LLM installed and load model weights from untrusted or unverified sources using pickle-based deserialization without cryptographic validation.
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 dataEnsure only trusted and verified model weights from known-good sources are loaded; implement cryptographic validation/signing of model artifacts before deserialization; consider replacing pickle-based deserialization with safer formats.
- Consultation6.0 h
- Implementation12.0 h
- Testing8.0 h
- Review / QA4.0 h
An estimate, not a bill — we confirm scope with you before any work starts. Need it this week? Rush from $8,448.
Scan for this in your stack
Free · runs locallyCheck whether your project pulls in CVE-2026-24233 — 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-2026-24233 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
Be the first to add a field note for this CVE — a mitigation you’ve verified, a version caveat, or a link to a working fix. Sign in above to contribute.
A place for practitioners to share what actually worked: a mitigation you’ve tested, a configuration change, a version- or environment-specific caveat, or a link to a verified patch. The most useful notes rise to the top as peers upvote them, so the signal stays high.
- Verified mitigations, workarounds, and config changes
- Version or environment caveats, and links to real fixes
- No weaponised exploit code, or anything meant to cause harm
- No spam, self-promotion, credentials, or personal data