vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Prior to 0.24.0, a frontend-legal multi-request speculative decoding workload can cause the rejection sampler to produce a recovered token equal to the model vocabulary size boundary value, which is then converted to negative one when the engine selects the next live token for a request and is written back into the drafter's input ids; that out-of-vocabulary value is later consumed by the model's embedding and attention path and crashes the engine worker with a GPU device-side assertion. The same triggering request sequence is reachable through the public gRPC Generate and Abort endpoints, so a remote client that can send generation requests can crash the shared engine worker, aborting concurrent requests and causing a service-wide denial of service for other clients of the deployment until the worker is restarted. This issue is fixed in version 0.24.0.
References
| Link | Resource |
|---|---|
| https://github.com/vllm-project/vllm/commit/8a5cf1ccd65e8ac7635c402c1ec0b08988bc26ca | Patch |
| https://github.com/vllm-project/vllm/pull/44744 | Issue Tracking Patch |
| https://github.com/vllm-project/vllm/security/advisories/GHSA-8wr5-jm2h-8r4f | Vendor Advisory Exploit |
| https://github.com/vllm-project/vllm/security/advisories/GHSA-8wr5-jm2h-8r4f | Vendor Advisory Exploit |
Configurations
History
No history.
Information
Published : 2026-07-06 21:16
Updated : 2026-07-07 19:04
NVD link : CVE-2026-54234
Mitre link : CVE-2026-54234
CVE.ORG link : CVE-2026-54234
JSON object : View
Products Affected
vllm
- vllm