VDB

CVE-2026-105754

CVE-2026-105754 PUBLISHED CVSS 6.5 MEDIUM

Reported by GitHub_M · Published October 5, 2026

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.

Risk Scores

CVSS 3.1
6.5
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Affected Products

VendorProductVersions
vllm-projectvllm< 0.30.0
vllm-projectvllm0
chainguardlmcache-cuda-12.8*
PyPIvllm0
chainguardpy3-vllm-cuda-12.4*
chainguardtritonserver-backend-vllm-cuda-13.00
chainguardvllm-cuda-13.20
chainguardvllm-openai-cuda-13.00

Timeline

  • Oct 5, 2026 CVE Published
  • Oct 6, 2026 EPSS Score
  • Oct 6, 2026 Coalition ESS Score
  • Oct 8, 2026 EPSS Score
  • Oct 8, 2026 CVE Updated
  • Oct 13, 2026 EPSS Score
  • Oct 16, 2026 EPSS Score
  • Oct 20, 2026 EPSS Score
  • Oct 22, 2026 EPSS Score
  • Oct 25, 2026 EPSS Score
  • Oct 29, 2026 EPSS Score
  • Oct 31, 2026 Security Advisory
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