vLLM EUVD-2026-83285

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated critical severity (CVSS 10.0

vllm-project vllm version v0.6.2 contains a vulnerability in the MessageQueue.dequeue() API function. Rated critical sev

Information exposure in vLLM inference engine versions 0.8.3 to before 0.14.1. Invalid image requests to the multimodal

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated high severity (CVSS 7.5), th

vLLM before version 0.14.1 contains a server-side request forgery vulnerability in the MediaConnector class where incons

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated medium severity (CVSS 6.5),

Uncontrolled resource consumption in vLLM's OpenAI-compatible completions endpoint allows any authenticated API client t

Vllm versions up to 0.12.0 is affected by allocation of resources without limits or throttling (CVSS 6.5).

Out-of-memory worker crashes in vLLM can be induced by a single small compressed audio payload submitted to the /v1/chat

Race condition in vLLM's prompt embedding loader allows concurrent API requests to bypass the sparse tensor invariant gu

Remote code execution in vLLM 0.10.1 through 0.13.x lets an attacker who controls the model repository or path run arbit

Server-Side Request Forgery in vLLM's multimodal MediaConnector allows remote attackers to coerce the inference server i