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CVE-2026-54234 Detail

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.


Metrics

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CVSS 4.0 Severity and Vector Strings:

NIST CVSS score
NIST: NVD
N/A
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References to Advisories, Solutions, and Tools

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URL Source(s) Tag(s)
GitHub, Inc. Patch 
GitHub, Inc. Issue Tracking  Patch 
CISA-ADP, GitHub, Inc. Exploit  Vendor Advisory 

Weakness Enumeration

CWE-ID CWE Name Source
CWE-20 Improper Input Validation GitHub, Inc.  
CWE-1284 Improper Validation of Specified Quantity in Input GitHub, Inc.  

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Change History

3 change records found show changes

Quick Info

CVE Dictionary Entry:
CVE-2026-54234
NVD Published Date:
07/06/2026
NVD Last Modified:
07/07/2026
Source:
GitHub, Inc.