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6.5
MEDIUM CVSS 3.1
CVE-2025-62426
vLLM vulnerable to DoS via large Chat Completion or Tokenization requests with specially crafted `chat_template_kwargs`
Description

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.

INFO

Published Date :

Nov. 21, 2025, 2:15 a.m.

Last Modified :

Dec. 4, 2025, 5:42 p.m.

Remotely Exploit :

Yes !

Source :

[email protected]
Affected Products

The following products are affected by CVE-2025-62426 vulnerability. Even if cvefeed.io is aware of the exact versions of the products that are affected, the information is not represented in the table below.

ID Vendor Product Action
1 Vllm vllm
: Total Affected Vendor : 1 | Products : 1
CVSS Scores
The Common Vulnerability Scoring System is a standardized framework for assessing the severity of vulnerabilities in software and systems. We collect and displays CVSS scores from various sources for each CVE.
Score Version Severity Vector Exploitability Score Impact Score Source
CVSS 3.1 MEDIUM [email protected]
CVSS 3.1 MEDIUM MITRE-CVE
Solution
Update vLLM to version 0.11.1 or later to fix denial of service vulnerability.
  • Update vLLM to version 0.11.1 or later.
  • Apply the latest security patches provided by the vendor.
  • Validate all input parameters before processing.
  • Monitor API server for abnormal processing delays.
References to Advisories, Solutions, and Tools

Here, you will find a curated list of external links that provide in-depth information, practical solutions, and valuable tools related to CVE-2025-62426.

URL Resource
https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/chat_utils.py#L1602-L1610 Product
https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/openai/serving_engine.py#L809-L814 Product
https://github.com/vllm-project/vllm/commit/3ada34f9cb4d1af763fdfa3b481862a93eb6bd2b Patch
https://github.com/vllm-project/vllm/pull/27205 Issue Tracking
https://github.com/vllm-project/vllm/security/advisories/GHSA-69j4-grxj-j64p Vendor Advisory
CWE - Common Weakness Enumeration

While CVE identifies specific instances of vulnerabilities, CWE categorizes the common flaws or weaknesses that can lead to vulnerabilities. CVE-2025-62426 is associated with the following CWEs:

Common Attack Pattern Enumeration and Classification (CAPEC)

Common Attack Pattern Enumeration and Classification (CAPEC) stores attack patterns, which are descriptions of the common attributes and approaches employed by adversaries to exploit the CVE-2025-62426 weaknesses.

CAPEC-125: Flooding Flooding CAPEC-130: Excessive Allocation Excessive Allocation CAPEC-147: XML Ping of the Death XML Ping of the Death CAPEC-197: Exponential Data Expansion Exponential Data Expansion CAPEC-229: Serialized Data Parameter Blowup Serialized Data Parameter Blowup CAPEC-230: Serialized Data with Nested Payloads Serialized Data with Nested Payloads CAPEC-231: Oversized Serialized Data Payloads Oversized Serialized Data Payloads CAPEC-469: HTTP DoS HTTP DoS CAPEC-482: TCP Flood TCP Flood CAPEC-486: UDP Flood UDP Flood CAPEC-487: ICMP Flood ICMP Flood CAPEC-488: HTTP Flood HTTP Flood CAPEC-489: SSL Flood SSL Flood CAPEC-490: Amplification Amplification CAPEC-491: Quadratic Data Expansion Quadratic Data Expansion CAPEC-493: SOAP Array Blowup SOAP Array Blowup CAPEC-494: TCP Fragmentation TCP Fragmentation CAPEC-495: UDP Fragmentation UDP Fragmentation CAPEC-496: ICMP Fragmentation ICMP Fragmentation CAPEC-528: XML Flood XML Flood

We scan GitHub repositories to detect new proof-of-concept exploits. Following list is a collection of public exploits and proof-of-concepts, which have been published on GitHub (sorted by the most recently updated).

Results are limited to the first 15 repositories due to potential performance issues.

The following list is the news that have been mention CVE-2025-62426 vulnerability anywhere in the article.

Results are limited to the first 20 news articles due to potential performance issues.

The following table lists the changes that have been made to the CVE-2025-62426 vulnerability over time.

Vulnerability history details can be useful for understanding the evolution of a vulnerability, and for identifying the most recent changes that may impact the vulnerability's severity, exploitability, or other characteristics.

  • Initial Analysis by [email protected]

    Dec. 04, 2025

    Action Type Old Value New Value
    Added CPE Configuration OR *cpe:2.3:a:vllm:vllm:0.11.1:rc0:*:*:*:*:*:* *cpe:2.3:a:vllm:vllm:0.11.1:rc1:*:*:*:*:*:* *cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:* versions from (including) 0.5.5 up to (excluding) 0.11.1
    Added Reference Type GitHub, Inc.: https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/chat_utils.py#L1602-L1610 Types: Product
    Added Reference Type GitHub, Inc.: https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/openai/serving_engine.py#L809-L814 Types: Product
    Added Reference Type GitHub, Inc.: https://github.com/vllm-project/vllm/commit/3ada34f9cb4d1af763fdfa3b481862a93eb6bd2b Types: Patch
    Added Reference Type GitHub, Inc.: https://github.com/vllm-project/vllm/pull/27205 Types: Issue Tracking
    Added Reference Type GitHub, Inc.: https://github.com/vllm-project/vllm/security/advisories/GHSA-69j4-grxj-j64p Types: Vendor Advisory
  • New CVE Received by [email protected]

    Nov. 21, 2025

    Action Type Old Value New Value
    Added Description 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.
    Added CVSS V3.1 AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
    Added CWE CWE-770
    Added Reference https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/chat_utils.py#L1602-L1610
    Added Reference https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/openai/serving_engine.py#L809-L814
    Added Reference https://github.com/vllm-project/vllm/commit/3ada34f9cb4d1af763fdfa3b481862a93eb6bd2b
    Added Reference https://github.com/vllm-project/vllm/pull/27205
    Added Reference https://github.com/vllm-project/vllm/security/advisories/GHSA-69j4-grxj-j64p
EPSS is a daily estimate of the probability of exploitation activity being observed over the next 30 days. Following chart shows the EPSS score history of the vulnerability.
Vulnerability Scoring Details
Base CVSS Score: 6.5
Attack Vector
Attack Complexity
Privileges Required
User Interaction
Scope
Confidentiality Impact
Integrity Impact
Availability Impact