vLLM 0.7.3 至 0.31.0(不含)版本存在一个不受控制的资源消耗漏洞。该漏洞允许远程客户端通过为每个请求提供 参数,来操控多模态处理器的配置。攻击者可在 Isaac、InternVL、H2OVL 及其他处理器中设置过大的图像尺寸、补丁数量、裁剪数量或帧数等值,从而耗尽系统内存并导致共享服务器终止运行。
Although we use advanced large model technology, its output may still contain inaccurate or outdated information.Shenlong tries to ensure data accuracy, but please verify and judge based on the actual situation.
| Vendor | Product | Affected Versions | CPE | Subscribe |
|---|---|---|---|---|
| vllm-project | vllm | 0.7.3 ~ 0.31.0 | - |
|
| # | POC Description | Source Link | Shenlong Link |
|---|
No public POC found.
Login to generate AI POC| CVE-2026-114053 | 8.1 HIGH | vLLM 0.7.3 before 0.31.0 RCE via mm_processor_kwargs.code_revision |
| CVE-2026-114050 | 7.5 HIGH | vLLM before 0.31.0 Remote Engine Crash via Forged Multimodal UUID |
| CVE-2026-114052 | 7.5 HIGH | vLLM 0.25.0 before 0.31.0 MOSS-Audio Memory Exhaustion via /tokenize mm_processor_kwargs |
| CVE-2026-114051 | 7.5 HIGH | vLLM before 0.31.0 Denial of Service via min_tokens in /inference/v1/generate |
| CVE-2026-114054 | 6.5 MEDIUM | vLLM before 0.31.0 Cross-Request Cache Aliasing via EXIF ImageID and Prefix-Cache Keys |
| CVE-2026-114049 | 3.7 LOW | vLLM before 0.31.0 Stale allowed_token_ids Mask via InputBatch.condense |
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