What’s Vulnerable › Vulnerabilities

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

• Severity: high (CVSS 7.3 v3.1)
• Exploited in the wild: not listed in CISA KEV
• EPSS: 0.3% probability of exploitation in the next 30 days
• Public exploit or detection template: none known
• Published: 2025-05-29 · updated 2026-06-17
• Weakness: CWE-1023

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.

Affected products

• vllm-project vllm: from 0.7.0 before 0.9.0 — fixed in 0.9.0

How to fix

Upgrade to a fixed version: vllm-project vllm 0.9.0. Check the vendor advisory for mitigations.

References

• https://github.com/vllm-project/vllm/commit/99404f53c72965b41558aceb1bc2380875f5d848
• https://github.com/vllm-project/vllm/pull/17378
• https://github.com/vllm-project/vllm/security/advisories/GHSA-c65p-x677-fgj6

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