EUVD-2026-78268
GHSA-2h9j-24rv-h4c7
Severity by source
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:P/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 Vendor (VulDB) · only source for this CVE.
CVSS VectorVendor: VulDB
Lifecycle Timeline
5DescriptionCVE.org
A vulnerability was determined in vllm-project vLLM up to 0.27.1. This affects an unknown part of the file /v1/chat/completions of the component Jinja Template Rendering. This manipulation of the argument chat_template causes resource consumption. The attack can be initiated remotely. The exploit has been publicly disclosed and may be utilized. The pull request to fix this issue awaits acceptance.
AnalysisAI
A resource-exhaustion (denial of service) flaw in vLLM's OpenAI-compatible /v1/chat/completions endpoint lets a caller- or model-supplied Jinja chat_template drive nested range() loops whose evaluation cost grows as O(N^depth), tying up a request-runtime worker thread for tens of seconds. vLLM releases up to and including 0.27.1 are affected (EUVD lists 0.27.0 and 0.27.1), with the CPE range expressed as vllm-project:vllm:* so earlier releases are plausibly in scope too. …
Unlock full vulnerability intelligence
- Risk assessment & exploitation conditions
- Attack chain visualization
- Remediation with exact patch versions
- Threat intelligence from 22 sources
- Personal watchlist & email alerts
Free forever · No credit card required
Threat intelligence, references, and detailed analysis are available after sign-in.
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),
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-400 – Uncontrolled Resource Consumption
View allSame technique Denial Of Service
View allShare
External POC / Exploit Code
Leaving vuln.today