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National Vulnerability Database

National Vulnerability Database

NVD

National Vulnerability Database
  1. Vulnerabilities

CVE-2025-46722 Detail

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.


Metrics

 
NVD enrichment efforts reference publicly available information to associate vector strings. CVSS information contributed by other sources is also displayed.
CVSS 4.0 Severity and Vector Strings:

NIST CVSS score
NIST: NVD
N/A
NVD assessment not yet provided.

References to Advisories, Solutions, and Tools

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Hyperlink Resource
https://github.com/vllm-project/vllm/commit/99404f53c72965b41558aceb1bc2380875f5d848 Patch 
https://github.com/vllm-project/vllm/pull/17378 Issue Tracking  Patch 
https://github.com/vllm-project/vllm/security/advisories/GHSA-c65p-x677-fgj6 Vendor Advisory 

Weakness Enumeration

CWE-ID CWE Name Source
CWE-1023 Incomplete Comparison with Missing Factors GitHub, Inc.  
CWE-1288 Improper Validation of Consistency within Input GitHub, Inc.  

Known Affected Software Configurations Switch to CPE 2.2

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Change History

2 change records found show changes

Quick Info

CVE Dictionary Entry:
CVE-2025-46722
NVD Published Date:
05/29/2025
NVD Last Modified:
06/24/2025
Source:
GitHub, Inc.