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CVE-2025-46722: vLLM has a Weakness in MultiModalHasher Image Hashing Implementation

4.2 CVSS

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.

Classification

CVE ID: CVE-2025-46722

CVSS Base Severity: MEDIUM

CVSS Base Score: 4.2

CVSS Vector: CVSS: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

Affected Products

Vendor: vllm-project

Product: vllm

Exploit Prediction Scoring System (EPSS)

EPSS Score: 0.06% (probability of being exploited)

EPSS Percentile: 19.16% (scored less or equal to compared to others)

EPSS Date: 2025-06-17 (when was this score calculated)

References

https://nvd.nist.gov/vuln/detail/CVE-2025-46722
https://github.com/vllm-project/vllm/security/advisories/GHSA-c65p-x677-fgj6
https://github.com/vllm-project/vllm/pull/17378
https://github.com/vllm-project/vllm/commit/99404f53c72965b41558aceb1bc2380875f5d848

Timeline