CVE-2025-46722

GHSA-c65p-x677-fgj6 HIGH
Published May 29, 2025

vLLM is an inference and serving engine for large language models (LLMs). In versions starting from 0.7.0 to before 0.9.0, in the file vllm/multimodal/hasher.py, the MultiModalHasher class has a...

Full analysis pending. Showing NVD description excerpt.

Affected Systems

Package Ecosystem Vulnerable Range Patched
vllm pip >= 0.7.0, < 0.9.0 0.9.0
vllm pip No patch

Severity & Risk

CVSS 3.1
7.3 / 10
EPSS
0.1%
chance of exploitation in 30 days
KEV Status
Not in KEV
Sophistication
N/A

Recommended Action

Patch available

Update vllm to version 0.9.0

Compliance Impact

Compliance analysis pending. Sign in for full compliance mapping when available.

Technical Details

NVD Description

vLLM is an inference and serving engine for large language models (LLMs). In versions starting from 0.7.0 to before 0.9.0, in the file vllm/multimodal/hasher.py, the MultiModalHasher class has a security and data integrity issue in its image hashing method. Currently, it serializes PIL.Image.Image objects using only obj.tobytes(), which returns only the raw pixel data, without including metadata such as the image’s shape (width, height, mode). As a result, two images of different sizes (e.g., 30x100 and 100x30) with the same pixel byte sequence could generate the same hash value. This may lead to hash collisions, incorrect cache hits, and even data leakage or security risks. This issue has been patched in version 0.9.0.

CVSS Vector

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L

Timeline

Published
May 29, 2025
Last Modified
June 24, 2025
First Seen
May 29, 2025