What’s Vulnerable › Vulnerabilities

CVE-2026-105754: vLLM: Scale-out disaggregated multimodal transport trusts caller-supplied features

• Severity: medium (CVSS 6.5 v3.1)
• Exploited in the wild: not listed in CISA KEV
• EPSS: 0.3% probability of exploitation in the next 30 days
• Public exploit or detection template: none known
• Published: 2026-10-05 · updated 2026-10-08
• Weakness: CWE-20

Description

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.

Affected products

• vllm-project vllm: before 0.30.0 — fixed in 0.30.0

How to fix

Upgrade to a fixed version: vllm-project vllm 0.30.0. Check the vendor advisory for mitigations.

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

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