[ CVE-2026-105754 · Published 5 Oct 2026 ]

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

CVSS 6.5 EPSS <1%1 agency advisoryFix available

[ Description ]Official 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.

[ Fixes ]Fixes and patches

  • vllm 0.30.0Fixed version · GitHub Advisories · pip package vllm: upgrade to 0.30.0 or later (affected < 0.30.0)
  • Patch on github.comVendor patch or bulletin · NVD · Reference tagged 'Patch' by NVD
  • Patch on github.comVendor patch or bulletin · NVD · Reference tagged 'Patch' by NVD

[ Advisories ]Government CERT advisories

  • BSI CERT-BundWID-SEC-2026-3564

    vllm: Multiple vulnerabilities Translated from German

    CVE-2026-105757CVE-2026-105754CVE-2026-105755+1 more CVEs

[ Packages ]Affected open-source packages