vLLM is an inference and serving engine for large language models. Prior to 0.30.0, structured-output request failures can escape request-scoped validation and reach the EngineCore fatal-error path. A per-request backend mismatch can re-raise a grammar compilation exception, padding produced by the ngram_gpu speculative-decoding mode can pass a negative token to guidance validation, and the Rust frontend can admit empty structured-output values that the Python frontend rejects, allowing ordinary constrained-generation requests to terminate the shared engine.
This issue is fixed in version 0.30.0.
Attack Parameters
Attack Vector
Network
Can be exploited remotely
Attack Complexity
Low
Easy to exploit
Privileges Required
Low
Basic privileges needed
User Interaction
None
No user interaction needed
Impact Assessment
Confidentiality
None
No data leak
Integrity
None
No data modification
Availability
High
Complete denial of service
CVSS Vector v3.1
Weakness Type (CWE)
Vulnerable Products
vllm-project:vllm
References 4
https://github.com/vllm-project/vllm/security/advisories/GHSA-85xf-c7hm-whqw
https://github.com/vllm-project/vllm/security/advisories/GHSA-85xf-c7hm-whqw
https://github.com/vllm-project/vllm/pull/51450
https://github.com/vllm-project/vllm/pull/51450
https://github.com/vllm-project/vllm/commit/c55e15a44ec4127832d4a86928a356fdd9e…
https://github.com/vllm-project/vllm/commit/c55e15a44ec4127832d4a86928a356fdd9e68dbd
https://github.com/vllm-project/vllm/releases/tag/v0.30.0
https://github.com/vllm-project/vllm/releases/tag/v0.30.0