CVE-2026-105754

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

State
Published
Severity
6.5 Medium
Published
Oct 5, 2026
Updated
Oct 8, 2026
Reserved
Oct 5, 2026

Read the record on cve.org

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.

Scores

CVSSScoreVectorSource
3.16.5 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:HGitHub_M

Weaknesses

  • CWE-20 Improper Input Validation
  • CWE-617 Reachable Assertion
  • CWE-639 Authorization Bypass Through User-Controlled Key
  • CWE-668 Exposure of Resource to Wrong Sphere
  • CWE-704 Incorrect Type Conversion or Cast
  • CWE-1284 Improper Validation of Specified Quantity in Input

Affected Products

ProductVersions
vllmvllm-project
  • < 0.30.0Affected

References

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