Improper Input Validation Affecting vllm-cuda-13.2 package, versions <0.32.0-r0

Note: Versions mentioned in the description apply only to the upstream vllm-cuda-13.2 package and not the vllm-cuda-13.2 package as distributed by Chainguard. See How to fix? for Chainguard relevant fixed versions and status.

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.

• 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