CVE-2025-46722
MEDIUMDescription
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.
How to fix
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Remediation is compiled from vendor and distribution security advisories. Always confirm against the linked source for your exact version and platform.
CVSS v3 Vector
Exploitability
Impact
CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:L
Exploit Intelligence
Low risk: more likely to be exploited than 19% of all known CVEs.
References
Related Vulnerabilities
Other CWE-1023 vulnerabilities, ordered by exploit likelihood. View all
| CVE | Severity | CVSS | EPSS | Exploited | Fix |
|---|---|---|---|---|---|
| CVE-2021-23146 | High | 7.1 | 0.9% | - | Fix |
| CVE-2026-7473 | Medium | 5.8 | 0.8% | KEV | - |
| CVE-2025-55333 | High | 6.1 | 0.8% | - | Fix |
| CVE-2026-4599 | Critical | 9.1 | 0.5% | - | Fix |
| CVE-2024-5528 | Low | 3.5 | 0.4% | - | Fix |
| CVE-2026-48587 | Low | 3.1 | 0.4% | - | Fix |
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Markdown
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This product uses NVD data but is not endorsed or certified by the NVD. EPSS scores courtesy of FIRST.org (https://www.first.org/epss). Source: CISA KEV Catalog. Data as of 2025-06-24.