HomeVllmCVE-2025-46722

CVE-2025-46722

MEDIUM
4.2CVSS
Published: 2025-05-29
Updated: 2025-06-24
AI Analysis

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 Metrics

Vector
CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:L
Attack Vector
network
Complexity
high
Privileges
low
User Action
none
Scope
unchanged
Confidentiality
low
Integrity
none
Availability
low
Weaknesses
CWE-1023CWE-1288

Metadata

Primary Vendor
VLLM
Published
5/29/2025
Last Modified
6/24/2025
Source
NIST NVD
Note: Verify all details with official vendor sources before applying patches.

Affected Products

vllm : vllm

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CVE-CVE-2025-46722 | MEDIUM Severity | CVEDatabase.com | CVEDatabase.com