CVE-2026-105754 PUBLISHED

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

Assigner: GitHub_M
Reserved: 05.10.2026 Published: 05.10.2026 Updated: 05.10.2026

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.

Metrics

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

Product Status

Vendor vllm-project
Product vllm
Versions
  • Version < 0.30.0 is affected

References

Problem Types

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