CVE-2024-53880
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 Triton Inference Server contains a vulnerability in the model loading API, where a user could cause an integer overflow or wraparound error by loading a model with an extra-large file size that overflows an internal variable. A successful exploit of this vulnerability might lead to denial of service.
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 Triton Inference Server has an integer overflow vulnerability in its model loading API. When a user loads a model with an excessively large file size, the size can overflow an internal variable used during the loading process, leading to potential denial of service.
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< 24.12CVSS 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
- None
- Integrity
- None
- Availability
- High
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/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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Identify Triton Inference Server installationRun 'docker ps' or check for triton server processes with 'ps aux | grep triton' or locate the triton executable pathAffected if Triton Inference Server is running or installed on the system
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Determine installed Triton versionCheck the version via 'tritonserver --version' or inspect the Docker image tag if running in container (e.g., 'docker images' shows the tag)Affected if The version number is lower than 24.12 (e.g., 24.11, 24.10, earlier versions)
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Verify model repository configurationCheck the model repository directory path configured in Triton (default is '/opt/triton/models' or check config.pbtxt for model_repository_path)Affected if A model repository is configured and accessible for loading models through the API
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Inspect model files for unusually large sizesList model files in the repository with 'ls -lh <model_path>' and check file sizes, especially in model directories under the repositoryAffected if Any model file exceeds a size that could cause integer overflow during the loading process (this is context-dependent but large files are the trigger)
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Review access to model loading APICheck if HTTP/gRPC model loading endpoints are exposed (default ports: 8000 for HTTP, 8001 for gRPC) via netstat or by examining Triton launch configurationAffected if The model loading API (POST /v2/repository/models/load) is accessible to users or automated systems
The environment is affected if Triton Inference Server version is below 24.12 AND the model loading API can be used to load models with excessively large file sizes from an accessible model repository.
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.
dbcve · scoped24.12
Apply the vendor patch when available, and implement input validation on model file sizes before loading to prevent overflow conditions.
NVIDIA Triton Inference Server 24.12
- 1. Back up the current Triton Inference Server configuration and model repository.
- 2. Stop the currently running Triton Inference Server instance.
- 3. Download the Triton Inference Server version 24.12 or later from the NVIDIA NGC catalog or GitHub releases.
- 4. Install the new version following the standard installation method for your deployment (Docker, pip, or native binary).
- 5. Verify the installation by running `tritonserver --version` to confirm the new version.
- 6. Start the Triton Inference Server service.
- 7. Load a test model to verify the server is functioning correctly.
Generated from the published advisory — verify against the referenced sources before acting.
- 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-2024-53880 — 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-2024-53880 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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- 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
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