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.
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Tue, 24 Jun 2025 18:30:00 +0000
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CPEs | cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:* | |
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Vllm
Vllm vllm |
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Thu, 29 May 2025 19:15:00 +0000
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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. | |
Title | vLLM has a Weakness in MultiModalHasher Image Hashing Implementation | |
Weaknesses | CWE-1023 CWE-1288 |
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cvssV3_1
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Status: PUBLISHED
Assigner: GitHub_M
Published:
Updated: 2025-05-29T18:13:02.824Z
Reserved: 2025-04-28T20:56:09.084Z
Link: CVE-2025-46722

Updated: 2025-05-29T18:12:51.565Z

Status : Analyzed
Published: 2025-05-29T17:15:21.523
Modified: 2025-06-24T18:12:30.023
Link: CVE-2025-46722
