CVE-2026-54234: vllm-project vllm Improper Input Validation

Vulnerability summary

CVE-2026-54234 is a high (CVSS 7.5) Improper Input Validation vulnerability in vllm-project vllm. EPSS estimates a 0.62% chance of exploitation in the next 30 days, higher than 48.5% of scored vulnerabilities. 1 public exploit is recorded.

Timeline

1. 06-07-2026
   PublishedVulnerability first disclosed.
2. 07-07-2026
   Last modifiedVulnerability information updated.

Description

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.

• CWE-20Improper Input ValidationThe product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.
• CWE-1284Improper Validation of Specified Quantity in InputThe product receives input that is expected to specify a quantity (such as size or length), but it does not validate or incorrectly validates that the quantity has the required properties.