TI-2025-29393 Aliases CVE-2025-46722 GHSA-c65p-x677-fgj6

vLLM has a Weakness in MultiModalHasher Image Hashing Implementation

LOW 32CWE-1023Published 2025-05-29 · Updated 2025-05-29

Key facts

Attack vector: Network
Privileges required: None
User interaction: None
Attack complexity: Low
Impact type: —
Confidentiality · integrity · availability: Low · Low · Low
Fix status: Partial Red Hat: 2 without a fix

Attack profile from CVSS 3.1 · NVD. Fix status from CNA, GitHub and Red Hat.

Summary

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.

Why it matters now

Exploitation None reported CISA SSVC · not in CISA KEV

EPSS 0.00338 27th percentileScored 2026-11-07

Fix Partial vllm (vllm-project): 0.9.0vllm (PyPI): 0.9.0Red Hat: 2 without a fix

SSVC decision points

CISA ADP · SSVC 2.0.3

Exploitation: None
Automatable: No
Technical impact: Partial

The decision (Track, Attend, Act) also depends on your mission and well-being impact; see CISA's SSVC guide.

Affected products

Vendor	Product	Affected versions	Fixed in	Status	Source
vllm-project	vllm	0.7.0 to before 0.9.0	0.9.0	Patch available	CNA
—	vllm (PyPI)	0.7.0 to before 0.9.0	0.9.0	Patch available	GHSA-c65p-x677-fgj6

vllm Vendorvllm-project Affected 0.7.0 to before 0.9.0 Fixed in 0.9.0 Patch available CNA
vllm (PyPI) Vendor— Affected 0.7.0 to before 0.9.0 Fixed in 0.9.0 Patch available GHSA-c65p-x677-fgj6

CPE configurations NVD · 1

CPE	Versions	Role	Vendor
cpe:2.3:a:vllm:vllm::::::::	0.7.0 to before 0.9.0	Vulnerable	vLLM

cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:* Versions 0.7.0 to before 0.9.0 Vulnerable ·vLLM

Remediation

Fix · CNA vllm (vllm-project) Fixed in 0.9.0
Fix · GHSA-c65p-x677-fgj6 vllm (PyPI) Fixed in 0.9.0 Advisory →

Vendor assessments

Red Hat

Red Hat rating: Moderate. Red Hat's page for this CVE →

Product	Status	Plan or fix
Red Hat AI Inference Server	Affected	No fix yet: Fix deferred
Red Hat Enterprise Linux AI (RHEL AI)	Affected	No fix yet: Fix deferred

Timeline

2026-10-02 Threatist record created from CVE-2025-46722
2026-10-03 Alias added: GHSA-c65p-x677-fgj6

References

Patch 2

Advisory 2

Other 2

TI-2024-10229 Remote Code Execution in vllm-project/vllm CVSS 3.0CRITICAL 9.8
TI-2024-08404 Remote Code Execution in vllm-project/vllm CVSS 3.1CRITICAL 9.8
TI-2026-36017 vLLM: OpenAI auth bypass CVSS 3.1CRITICAL 9.1
TI-2026-63056 vLLM before 0.28.0 Denial of Service via negative token ID CVSS 4.0HIGH 8.7
TI-2026-04308 Hardcoded trust_remote_code=True in vllm-project/vllm Bypasses User Security Control CVSS 3.0HIGH 8.8
TI-2026-39137 vLLM: Remote DoS in vLLM via Invalid Recovered Token Reinjection CVSS 3.1HIGH 7.5

See all 124 →

Sources

This product uses the NVD API but is not endorsed or certified by the NVD. Sources and licences