CVE-2026-44222

6.5 MEDIUM
Published: May 12, 2026 Modified: May 14, 2026
View on NVD

Description

vLLM is an inference and serving engine for large language models (LLMs). From 0.6.1 to before 0.20.0, there is a a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on image_grid_thw/video_grid_thw are affected. This vulnerability is fixed in 0.20.0.

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CVSS v3.x Details

0.0 Low Medium High Critical 10.0
Vector String
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

References to Advisories, Solutions, and Tools

Patch Vendor Advisory Exploit Third Party Advisory
https://github.com/vllm-project/vllm/issues/32656
Source: security-advisories@github.com
Issue Tracking
https://github.com/vllm-project/vllm/security/advisories/GHSA-hpv8-x276-m59f
Source: security-advisories@github.com
Exploit Vendor Advisory

2 reference(s) from NVD

Quick Stats

CVSS v3 Score
6.5 / 10.0
EPSS (Exploit Probability)
0.4%
33th percentile
Exploitation Status
Not in CISA KEV

Weaknesses (CWE)

Affected Vendors

vllm