vllm-project vLLM up to 0.31.x Multimodal Caching pickle.loads monotonic_id deserialization

Summaryinfo

A vulnerability described as critical has been identified in vllm-project vLLM up to 0.31.x. This vulnerability affects the function pickle.loads of the component Multimodal Caching. Executing a manipulation of the argument monotonic_id can lead to deserialization. This vulnerability is registered as CVE-2026-115912. It is possible to launch the attack remotely. No exploit is available. Upgrading the affected component is recommended.

Detailsinfo

A vulnerability has been found in vllm-project vLLM up to 0.31.x and classified as critical. This vulnerability affects the function pickle.loads of the component Multimodal Caching. The manipulation of the argument monotonic_id with an unknown input leads to a deserialization vulnerability. The CWE definition for the vulnerability is CWE-502. The product deserializes untrusted data without sufficiently verifying that the resulting data will be valid. As an impact it is known to affect confidentiality, integrity, and availability. CVE summarizes:

vLLM is an inference and serving engine for large language models. Prior to 0.32.0, an authenticated caller can place forged shared-memory address and monotonic_id values in public multimodal features.kwargs_data. In scale-out deployments with shared-memory multimodal caching enabled, workers trust those values and pass attacker-selected bytes to pickle.loads(), allowing arbitrary Python execution even when insecure serialization is disabled. This issue is fixed in version 0.32.0.

The advisory is available at github.com. This vulnerability was named CVE-2026-115912 since 10/23/2026. The exploitation appears to be easy. The attack can be initiated remotely. Technical details are known, but there is no available exploit.

Upgrading to version 0.32.0 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
• 0.31

Website

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

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