Bug 2369221 (CVE-2025-46722)

Summary: CVE-2025-46722 vllm: vLLM has a Weakness in MultiModalHasher Image Hashing Implementation
Product: [Other] Security Response Reporter: OSIDB Bzimport <bzimport>
Component: vulnerabilityAssignee: Product Security DevOps Team <prodsec-dev>
Status: NEW --- QA Contact:
Severity: medium Docs Contact:
Priority: medium    
Version: unspecifiedCC: alinfoot, bbrownin, dtrifiro, rbryant, weaton
Target Milestone: ---Keywords: Security
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Hardware: All   
OS: Linux   
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An image based hash collision flaw was found in vLLM due to insufficient distinct hashing. This flaw allows an attacker to poison the cache in a vLLM instance, which may lead to inconsistent or unexpected output.
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oVirt Team: --- RHEL 7.3 requirements from Atomic Host:
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Description OSIDB Bzimport 2025-05-29 17:01:11 UTC 
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.