CVE-2026-105754CWE-1284CWE-20CWE-617CWE-639CWE-668CWE-704

CVE-2026-105754

Medium · published October 6, 2026

CVSS v3.1
6.5
EPSS
0%
Percentile
18.1
In the wild
Unconfirmed
What it is

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.

The record
Technical detail
CVSS v3.1
6.5 · MEDIUM
Vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
CVSS v4.0
Not supplied
EPSS
0.00269 · 18.1th percentile
Weaknesses
CWE-1284 · Improper Validation of Specified Quantity in Input; 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
Published
2026-10-06T03:17Z
Affected products (1)
ProductVersionsFixed in
vllm/vllm< 0.30.00.30.0
References (4)
EPSS history
Timeline
  • 05 OCT 22:46Z
    vLLM: Scale-out disaggregated multimodal transport trusts caller-supplied features
    cvelistv5
  • 05 OCT 22:46Z
    vLLM: Scale-out disaggregated multimodal transport trusts caller-supplied features
    cvelistv5