CVE-2025-46722

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.

The following products are affected by CVE-2025-46722 vulnerability.

Here, you will find a curated list of external links that provide in-depth information to CVE-2025-46722.

URL	Resource
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
https://nvd.nist.gov/vuln/detail/CVE-2025-46722
https://www.cve.org/CVERecord?id=CVE-2025-46722