MEDIUM 6.5 NVD
CVE-2026-105754
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the /inference/v1/generate endpoint in the disaggregated scale-out path acce
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the /inference/v1/generate endpoint in the disaggregated scale-out path accepts caller-supplied tensors in the features.kwargs_data field, cache identifiers in the features.mm_hashes field, ranges in the features.mm_placeholders field, and wire-selected multimodal field processors without rebinding them to the active model renderer contract. Forged grid geometry, field types, or non-positive placeholder lengths can terminate the shared EngineCore; when an attacker knows or can induce a victim's content hash, forged cache hashes can poison or retrieve cross-request encoder-cache state; and dropped sparse placeholder masks can alter replayed transport semantics. This issue is fixed in version 0.30.0.
References
- https://github.com/vllm-project/vllm/commit/1970f3ed4be7fa8620e4ddc4a12c36a8384cfc27
- https://github.com/vllm-project/vllm/pull/51898
- https://github.com/vllm-project/vllm/releases/tag/v0.30.0
- https://github.com/vllm-project/vllm/security/advisories/GHSA-ph72-cqr5-qpp7
This medium severity vulnerability with a CVSS score of 6.5 was published on 2026-10-05 via NVD.
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