vllm-project vLLM up to 0.30.x Multimodal Preprocessing mm_processor_kwargs.max_patches resource consumption

Summaryinfo

A vulnerability, which was classified as problematic, was found in vllm-project vLLM up to 0.30.x. This vulnerability affects unknown code of the component Multimodal Preprocessing. Such manipulation of the argument mm_processor_kwargs.max_patches leads to resource consumption. This vulnerability is uniquely identified as CVE-2026-126397. The attack can be launched remotely. No exploit exists. You should upgrade the affected component.

Detailsinfo

A vulnerability was found in vllm-project vLLM up to 0.30.x and classified as problematic. Affected by this issue is some unknown functionality of the component Multimodal Preprocessing. The manipulation of the argument mm_processor_kwargs.max_patches with an unknown input leads to a resource consumption vulnerability. Using CWE to declare the problem leads to CWE-400. The product does not properly control the allocation and maintenance of a limited resource. Impacted is availability. CVE summarizes:

vLLM from 0.10.1 before 0.31.0 forwards a request-controlled mm_processor_kwargs.max_patches value to the Hugging Face processor for Cohere2 Vision models without a server-side upper bound, causing quadratic CPU work during multimodal preprocessing. Remote clients can send small image chat-completion requests with large, varying max_patches values to bypass the LRU cache, exhaust preprocessing workers, and deny service to other clients.

The advisory is available at github.com. This vulnerability is handled as CVE-2026-126397 since 11/10/2026. The exploitation is known to be easy. The attack may be launched remotely. No form of authentication is required for exploitation. Technical details are known, but there is no available exploit. This vulnerability is assigned to T1499 by the MITRE ATT&CK project.

Upgrading to version 0.31.0 eliminates this vulnerability.

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Productinfo

Type

• Project Management Software

Vendor

• vllm-project

Name

• vLLM

Version

• 0.1
• 0.2
• 0.3
• 0.4
• 0.5
• 0.6
• 0.7
• 0.8
• 0.9
• 0.10
• 0.11
• 0.12
• 0.13
• 0.14
• 0.15
• 0.16
• 0.17
• 0.18
• 0.19
• 0.20
• 0.21
• 0.22
• 0.23
• 0.24
• 0.25
• 0.26
• 0.27
• 0.28
• 0.29
• 0.30

Website

• Product: https://github.com/vllm-project/vllm/

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