Description
vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before 0.11.1, users can crash the vLLM engine serving multimodal models by passing multimodal embedding inputs with correct ndim but incorrect shape (e.g. hidden dimension is wrong), regardless of whether the model is intended to support such inputs (as defined in the Supported Models page). This issue has been patched in version 0.11.1.
CVSS Metrics
- Vector
- CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:H/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
- Attack Vector
- network
- Complexity
- low
- Privileges
- low
- User Action
- none
- Confidentiality
- undefined
- Integrity
- undefined
- Availability
- undefined
- Weaknesses
- CWE-129
Metadata
- Primary Vendor
- VLLM
- Published
- 11/21/2025
- Last Modified
- 12/4/2025
- Source
- NIST NVD
- Note: Verify all details with official vendor sources before applying patches.
Affected Products
vllm : vllmvllm : vllmvllm : vllm
AI-Powered Remediation
Generate remediation guidance or a C-suite brief for this vulnerability.