CVE-2025-46722

vLLM has a Weakness in MultiModalHasher Image Hashing Implementation

MEDIUM 4.2

At a glance

Source
NVD
EPSS
EPSS 0.34 % · percentile 27 ()
State
PUBLISHED
Published
Updated
Assigner
GitHub_M
NVD status
Analyzed

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.

Weaknesses (CWE)

  • CWE-1288 — Improper Validation of Consistency within Input
  • CWE-1023 — Incomplete Comparison with Missing Factors

References

Scores

VersionScoreVectorScored by
CVSS 3.1MEDIUM 4.2CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:LGitHub_M
CVSS 3.1MEDIUM 4.2CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:LNVD · security-advisories@github.com (Secondary)
CVSS 3.1HIGH 7.3CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:LNVD · nvd@nist.gov (Primary)

Advisories

European references

Affected configurations (NVD CPE)

Affected products

ProductVersions
vllm-project · vllmaffected >= 0.7.0, < 0.9.0

Sources

SourceLicence
CVE List (CVEProject cvelistV5)CVE Program Terms of Use
EPSS (FIRST.org)FIRST — attribution requested
ENISA EUVDENISA — reproduction authorised, source acknowledged
NVD (NIST) via fkie-cad/nvd-json-data-feedsPublic domain — this product uses data from the NVD API but is not endorsed or certified by the NVD