CVE-2026-54234
Fixed in a distro release EPSS <1%CVSS 7.5
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.
Products: vllm-project vllm
Fixed upstream
| Ecosystem | Package | Fixed in |
|---|---|---|
| PyPI | vllm | 0.24.0 |
Distro releases
| Release | Package | Fixed in |
|---|---|---|
| Chainguard | lmcache-cuda-12.8 | no fix |
| Chainguard | nemo-rl-cuda-13.0-vllm | no fix |
| Chainguard | py3.10-vllm-cuda-12.4 | no fix |
| Chainguard | py3.12-vllm-cuda-12.4 | no fix |
| Chainguard | tritonserver-backend-vllm-cuda-13.0 | 25.11-r12 |
| Chainguard | vllm-openai-cuda-12.9 | 0.28.0-r0 |
| Chainguard | vllm-openai-cuda-12.9-nvidia-cusparse | no fix |