Vllm
CVE-2025-48956
HIGH
Severity by source
AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Primary rating from GitHub Advisory.
CVSS VectorGitHub Advisory
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Lifecycle Timeline
3DescriptionGitHub Advisory
vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.10.1.1, a Denial of Service (DoS) vulnerability can be triggered by sending a single HTTP GET request with an extremely large header to an HTTP endpoint. This results in server memory exhaustion, potentially leading to a crash or unresponsiveness. The attack does not require authentication, making it exploitable by any remote user. This vulnerability is fixed in 0.10.1.1.
AnalysisAI
vLLM is an inference and serving engine for large language models (LLMs). Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. This Uncontrolled Resource Consumption vulnerability could allow attackers to cause denial of service by exhausting system resources.
Technical ContextAI
This vulnerability is classified as Uncontrolled Resource Consumption (CWE-400), which allows attackers to cause denial of service by exhausting system resources. vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.10.1.1, a Denial of Service (DoS) vulnerability can be triggered by sending a single HTTP GET request with an extremely large header to an HTTP endpoint. This results in server memory exhaustion, potentially leading to a crash or unresponsiveness. The attack does not require authentication, making it exploitable by any remote user. This vulnerability is fixed in 0.10.1.1. Affected products include: Vllm. Version information: before 0.10.1.1.
RemediationAI
A vendor patch is available. Apply the latest security update as soon as possible. Implement rate limiting, set resource quotas, validate input sizes, use timeouts.
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
Information exposure in vLLM inference engine versions 0.8.3 to before 0.14.1. Invalid image requests to the multimodal
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
Same weakness CWE-400 – Uncontrolled Resource Consumption
View allSame technique Denial Of Service
View allVendor StatusVendor
Share
External POC / Exploit Code
Leaving vuln.today