CVE-2025-23320
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 for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause the shared memory limit to be exceeded by sending a very large request. A successful exploit of this vulnerability might lead to 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 Triton Inference Server's Python backend does not properly validate request sizes before allocating shared memory, allowing an attacker to send excessively large requests that exceed configured shared memory limits and potentially expose sensitive data from memory allocations.
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< 25.07CVSS 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
- None
- Availability
- None
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N
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 'tritonserver --version' or check the Docker image tag if running in a containerAffected if Triton Inference Server is present and version is below 25.07
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Verify Python backend is enabledCheck model configuration files in the model repository for 'backend: python' or inspect running Triton configuration via the metrics endpointAffected if The Python backend is loaded and handling inference requests
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Examine shared memory configurationInspect the Triton configuration file (config.pbtxt) or environment variables for 'cuda_shared_memory' or 'cpu_shared_memory' settings and their allocated sizesAffected if Shared memory is configured with limits that could be exceeded by large requests
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Check for request size validation in Python backendReview any custom Python model code for explicit payload size checks or maximum request length validation before shared memory allocationAffected if No request size validation exists in the Python backend code before shared memory operations
You are affected if Triton Inference Server version is below 25.07, the Python backend is enabled, and no explicit request size validation exists before shared memory allocation.
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 · scoped25.07
Implement strict request size validation and enforce configured shared memory limits in the Python backend; consider adding explicit maximum payload size checks before memory allocation.
25.07
- 1. Identify the current Triton Inference Server version currently deployed
- 2. Back up all custom configurations, models, and any critical data
- 3. Stop the Triton Inference Server service
- 4. Upgrade Triton Inference Server to version 25.07 or later using NVIDIA's official upgrade procedures
- 5. Verify the version upgrade was successful by checking the server version
- 6. Start the Triton Inference Server service
- 7. Test with large requests to confirm the shared memory limit issue is resolved and no information disclosure occurs
Generated from the published advisory — verify against the referenced sources before acting.
- Consultation4.0 h
- Implementation8.0 h
- Testing4.0 h
- Review / QA2.0 h
An estimate, not a bill — we confirm scope with you before any work starts. Need it this week? Rush from $5,120.
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Free · runs locallyCheck whether your project pulls in CVE-2025-23320 — 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-2025-23320 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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