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How the CVE Works
CVE-2025-33211 is an improper input validation flaw within the NVIDIA Triton Inference Server. The vulnerability exists in the component responsible for parsing and validating client request parameters related to data quantities, such as batch sizes or input tensor dimensions. An attacker can craft a specially designed inference request containing a malformed or extreme numerical value for these parameters. The server’s validation logic fails to properly check the bounds or sanity of this user-supplied value before passing it to internal allocation and processing routines. This causes the server to attempt operations based on the invalid quantity, such as trying to allocate an incorrectly sized memory buffer or process an impossible batch configuration. This triggers an internal error condition that the server cannot handle gracefully, leading to a crash and a complete denial of service (DoS) for all users. The exploit is network-triggerable and does not require authentication, allowing a remote attacker to disrupt AI inference services.
DailyCVE Form
Platform: NVIDIA Triton Server
Version: Linux versions
Vulnerability: Improper Input Validation
Severity: Medium
date: 2025-12-03
Prediction: Patch by 2025-12-17
What Undercode Say
`$ curl -X POST http://
`$ docker logs triton-server-1 | grep -i “error\|panic\|fault”`
` Check for process crash and automatic restart loops in system logs.`
How Exploit
Craft an inference request payload with an invalid ‘batch_size’ parameter set to an extreme negative value, a massively large integer, or a non-numeric string. Send this payload to the Triton Server’s HTTP or gRPC inference endpoint. The server parses the request but fails to validate the batch size, leading to a segmentation fault or an unhandled exception that terminates the main server process.
Protection from this CVE
Apply NVIDIA security update. Implement network segmentation. Use a reverse proxy/WAF for request filtering. Restrict server access to trusted clients.
Impact
Denial of Service (DoS) causing service disruption, failed inference requests, and potential operational downtime for dependent AI applications.
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Sources:
Reported By: nvd.nist.gov
Extra Source Hub:
Undercode
