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CVE-2026-105754: CWE-20: Improper Input Validation in vllm-project vllm

0
Medium
Published: 10/05/2026 (10/05/2026, 22:46:03 UTC)
Source: CVE Database V5
Vendor/Project: vllm-project
Product: vllm

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.

CVSS v3.1

Score 6.5medium

Attack Vector
Network
Attack Complexity
Low
Privileges Required
Low
User Interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Affected software

vllm-project

vllm

Affected versions
<0.30.0

Technical Details

Data Version
5.2
Assigner Short Name
GitHub_M
Date Reserved
2026-10-05T19:11:07.947Z
Cvss Version
3.1
State
PUBLISHED

Threat ID: 6ac42ce12cdf04f656446bf2

Added to database: 10/05/2026, 23:04:01 UTC

Last updated: 10/05/2026, 23:04:01 UTC

Views: 1

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