Vllm
CVE-2025-62426
MEDIUM
Vulnerability Assessment
| Weakness |
CWE
OWASP Top 10:2025
A06:2025 - Insecure Design
ASVS 5.0
V2 - Validation and Business Logic Resource allocation limits
MITRE ATT&CK
1 technique across 1 tactic: Impact The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up. |
| Impact | CVSS score of 6.5 (MEDIUM). Attack characteristics: network attack vector, low complexity, low privileges required, no user interaction not required. EPSS exploitation probability: 0.09%. |
| Exploit ScenarioDerived | A remote attacker could exhaust system resources through uncontrolled allocation by sending crafted requests to the affected application. |
Get an e-mail when this CVE is exploited, patched or re-scored. Free account.
Alert meAnalysisAIv1 – Mar 28, 2026
vLLM is an inference and serving engine for large language models (LLMs). Rated medium severity (CVSS 6.5), this vulnerability is remotely exploitable, low attack complexity. This Allocation of Resources Without Limits vulnerability could allow attackers to exhaust system resources through uncontrolled allocation.
RemediationAI
A vendor patch is available. Apply the latest security update as soon as possible. Set resource limits, implement rate limiting, validate input sizes.
Affected Packages
Source: OSV (Open Source Vulnerabilities)
Affects your stack
Affects 1 package in pypi
Checking this CVE against your own stacks and servers is part of the Basic plan and up.
Technical ContextAI · show details
This vulnerability is classified as Allocation of Resources Without Limits (CWE-770), which allows attackers to exhaust system resources through uncontrolled allocation. vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before 0.11.1, the /v1/chat/completions and /tokenize endpoints allow a chat_template_kwargs request parameter that is used in the code before it is properly validated against the chat template. With the right chat_template_kwargs parameters, it is possible to block processing of the API server for long periods of time, delaying all other requests. This issue has been patched in version 0.11.1. Affected products include: Vllm. Version information: version 0.5.5.
Know when Vllm gets its next vulnerability
- Watch products and get an email when something new is published
- Check this CVE against your own stack and servers
- Exploit and PoC references, threat intelligence from 22 sources
No credit card · 7-day full trial
Severity by source
AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Primary rating from Vendor (github).
CVSS VectorVendor: github
Lifecycle Timeline
2DescriptionCVE.org
vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before 0.11.1, the /v1/chat/completions and /tokenize endpoints allow a chat_template_kwargs request parameter that is used in the code before it is properly validated against the chat template. With the right chat_template_kwargs parameters, it is possible to block processing of the API server for long periods of time, delaying all other requests. This issue has been patched in version 0.11.1.
Blast Radius
ecosystem impact- 2 pypi packages depend on vllm (2 direct, 0 indirect)
Ecosystem-wide dependent count for version 0.5.5.
Executive ViewAI
Organizations using the code should be aware of notable risk from CVE-2025-62426 rated CVSS 6.5. Exploitation could compromise the security of affected deployments. A patch is available and should be applied during regular vulnerability management.
Apply vendor patch. If patching is delayed: restrict network access and enable monitoring.
Threat Intelligence Overview
NVD
CVE.org
EPSS
GitHub
How this decision was reached
The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.
What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
Denial of Service attacks render applications or systems unavailable by overwhelming resources or triggering failure conditions.
Affected Products (CPE)CVE.org
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
Timing-based API key recovery in vLLM before 0.11.0rc2 can let remote attackers bypass bearer-token authentication on de
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated high severity (CVSS 7.5), th
Denial of service in the vLLM LLM inference server (all versions prior to 0.24.0) allows a remote client to crash the sh
vLLM before version 0.14.1 contains a server-side request forgery vulnerability in the MediaConnector class where incons
Out-of-memory worker crashes in vLLM can be induced by a single small compressed audio payload submitted to the /v1/chat
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated medium severity (CVSS 6.5),
GPU memory exhaustion in vLLM versions prior to 0.28.0 lets an attacker with low-privileged API access (PR:L in the CVSS
Multimodal inference requests to vLLM deployments running versions prior to 0.28.0 can trigger a process-level assertion
Uncontrolled resource consumption in vLLM's OpenAI-compatible completions endpoint allows any authenticated API client t
Exploit-DB