Speed Up XSS Scanning of APIs

TL;DR

Scanning websites that return API responses (like JSON) for Cross-Site Scripting (XSS) can be slow. This guide shows how to make your scanner faster by focusing on the data being returned, using efficient tools, and handling rate limiting.

Improving XSS Scanner Performance

1. Understand the API Response Structure
• Before scanning, examine the API’s response format (JSON, XML, etc.). Knowing where user-controlled data appears is crucial.
• Use tools like curl or Postman to inspect responses for different inputs.

curl -X GET "https://example.com/api?search=test"

2. Target Data, Not Entire Pages
• Instead of requesting full pages, only request the API endpoints that handle user input. This drastically reduces data transfer and processing time.
• Focus on parameters in URLs, POST body data, and any other areas where users can provide information.
3. Efficient Payload Injection
• Use a minimal set of XSS payloads to start with. Common payloads include: , "onload=alert(1), and <img src=x onerror=alert(1)>
• Avoid unnecessary encoding or decoding during payload injection. Let the API handle that if it’s designed to do so.
4. Parallel Requests (Carefully!)
• Making multiple requests in parallel can significantly speed up scanning, but be mindful of rate limiting and server load.
• Use a library or framework that supports asynchronous HTTP requests (e.g., asyncio in Python).

import asyncio
import aiohttp

async def scan_endpoint(url):
    async with aiohttp.ClientSession() as session:
        try:
            response = await session.get(url)
            # Process response for XSS
        except Exception as e:
            print(f"Error scanning {url}: {e}")

async def main():
    urls = ["https://example.com/api?param1=value", "https://example.com/api?param2=another_value"]
    tasks = [scan_endpoint(url) for url in urls]
    await asyncio.gather(*tasks)

asyncio.run(main())

• Start with a small number of concurrent requests and gradually increase it until you hit rate limits or experience performance issues.
5. Rate Limiting Handling
• APIs often have rate limits to prevent abuse. Your scanner needs to handle these gracefully.
• Check the API’s documentation for rate limit information (e.g., maximum requests per minute).
• Implement a delay or retry mechanism if you encounter rate limit errors (HTTP status code 429 Too Many Requests).
6. Optimized Parsing of Responses
• If the API returns JSON, use an efficient JSON parsing library. Python’s built-in json module is generally good enough but consider alternatives like ujson for performance gains if needed.
• Focus your XSS detection logic on the specific fields within the JSON response that contain user input. Avoid scanning irrelevant data.
7. Caching (Use with Caution)
• If you’re repeatedly requesting the same API endpoints with different payloads, consider caching responses to reduce redundant requests.
• Be careful when caching: ensure that cached responses are still valid and don’t lead to false negatives if the API behavior changes.
8. Use a Dedicated XSS Scanner Library
• Consider using existing XSS scanner libraries designed for APIs, such as those integrated into Burp Suite or OWASP ZAP. These tools often have built-in optimizations and features specifically tailored for API scanning.