CVE-2026-115912
HIGH
7,5
Source: [email protected]
Attack Vector: network
Attack Complexity: high
Privileges Required: low
User Interaction: none
Scope: unchanged
Confidentiality: high
Integrity: high
Availability: high
Description
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.
502
Deserialization of Untrusted Data
DraftCommon Consequences
Security Scopes Affected:
Integrity
Availability
Other
Potential Impacts:
Modify Application Data
Unexpected State
Dos: Resource Consumption (Cpu)
Varies By Context
Applicable Platforms
Languages:
Java, Ruby, PHP, Python, JavaScript
Technologies:
Not Technology-Specific, ICS/OT, AI/ML
https://github.com/vllm-project/vllm/commit/3d5625cc3c2474994107047d7a6ecb8e400…
https://github.com/vllm-project/vllm/pull/59357
https://github.com/vllm-project/vllm/releases/tag/v0.32.0
https://github.com/vllm-project/vllm/security/advisories/GHSA-6757-hggm-hp2w