Vllm
CVE-2026-22778
CRITICAL
GHSA-4r2x-xpjr-7cvv
Severity by source
AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
Primary rating from Vendor (github).
CVSS VectorVendor: github
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
Lifecycle Timeline
3Blast Radius
ecosystem impact- 1 pypi packages depend on vllm (1 direct, 0 indirect)
Ecosystem-wide dependent count for version 0.8.3.
DescriptionCVE.org
vLLM is an inference and serving engine for large language models (LLMs). From 0.8.3 to before 0.14.1, when an invalid image is sent to vLLM's multimodal endpoint, PIL throws an error. vLLM returns this error to the client, leaking a heap address. With this leak, we reduce ASLR from 4 billion guesses to ~8 guesses. This vulnerability can be chained a heap overflow with JPEG2000 decoder in OpenCV/FFmpeg to achieve remote code execution. This vulnerability is fixed in 0.14.1.
AnalysisAI
Information exposure in vLLM inference engine versions 0.8.3 to before 0.14.1. Invalid image requests to the multimodal endpoint cause sensitive data logging. Patch available.
Technical ContextAI
CWE-532 information exposure through log files. Invalid image requests trigger logging of sensitive data.
RemediationAI
Update to vLLM 0.14.1.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated critical severity (CVSS 10.0
vllm-project vllm version v0.6.2 contains a vulnerability in the MessageQueue.dequeue() API function. Rated critical sev
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated high severity (CVSS 7.5), th
vLLM before version 0.14.1 contains a server-side request forgery vulnerability in the MediaConnector class where incons
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated medium severity (CVSS 6.5),
Vllm versions up to 0.12.0 is affected by allocation of resources without limits or throttling (CVSS 6.5).
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
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated critical severity (CVSS 9.0)
Remote code execution in vLLM versions prior to 0.22.1 allows attackers to backdoor production LLM inference deployments
Remote code execution is possible in vLLM inference and serving engine versions 0.10.1 through 0.17.x due to hardcoded t
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External POC / Exploit Code
Leaving vuln.today