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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.

PUBLISHED Reserved 2025-04-28 | Published 2025-05-29 | Updated 2025-05-29 | Assigner GitHub_M




MEDIUM: 4.2CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:L

Problem types

CWE-1288: Improper Validation of Consistency within Input

CWE-1023: Incomplete Comparison with Missing Factors

Product status

>= 0.7.0, < 0.9.0
affected

References

github.com/...t/vllm/security/advisories/GHSA-c65p-x677-fgj6

github.com/vllm-project/vllm/pull/17378

github.com/...ommit/99404f53c72965b41558aceb1bc2380875f5d848

cve.org (CVE-2025-46722)

nvd.nist.gov (CVE-2025-46722)

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