Vllm
CVE-2025-62372
HIGH
GHSA-pmqf-x6x8-p7qw
Severity by source
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
Primary rating from GitHub Advisory.
CVSS VectorGitHub Advisory
Lifecycle Timeline
3Blast Radius
ecosystem impact- 2 pypi packages depend on vllm (2 direct, 0 indirect)
Ecosystem-wide dependent count for version 0.5.5.
DescriptionGitHub Advisory
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.
AnalysisAI
vLLM is an inference and serving engine for large language models (LLMs). Rated high severity (CVSS 8.3), this vulnerability is remotely exploitable, low attack complexity.
Technical ContextAI
This vulnerability is classified under CWE-129. 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. Affected products include: Vllm. Version information: version 0.5.5.
RemediationAI
A vendor patch is available. Apply the latest security update as soon as possible. Apply vendor patches when available. Implement network segmentation and monitoring as interim mitigations.
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Uncontrolled resource consumption in vLLM's OpenAI-compatible completions endpoint allows any authenticated API client t
Vllm versions up to 0.12.0 is affected by allocation of resources without limits or throttling (CVSS 6.5).
Race condition in vLLM's prompt embedding loader allows concurrent API requests to bypass the sparse tensor invariant gu
Remote code execution in vLLM 0.10.1 through 0.13.x lets an attacker who controls the model repository or path run arbit
Server-Side Request Forgery in vLLM's multimodal MediaConnector allows remote attackers to coerce the inference server i
Denial of service in vllm 0.19.0's OpenAI-compatible serving path allows remote unauthenticated attackers to exhaust sch
Same weakness CWE-129 – Improper Validation of Array Index
View allSame technique Denial Of Service
View allVendor StatusVendor
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External POC / Exploit Code
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