PyFuzz: My Journey Building an API Fuzzer

Hey there, fellow tech enthusiasts! Today, I’m diving into a project that’s been a game-changer for me in my cybersecurity journey: PyFuzz. It’s a simple API endpoint enumerator I built to uncover hidden API routes by sending HTTP requests using a wordlist. If you’re into security research or penetration testing, this tool might just become your new best friend. Let’s break down why I created it, how it works, and what I learned along the way.

Why I Built PyFuzz

APIs are everywhere these days—they power the apps we use, the services we rely on, and even the backend of most websites. But here’s the thing: they’re often a goldmine for vulnerabilities. Hidden endpoints, misconfigurations, or even forgotten API routes can expose sensitive data or functionality if left unchecked. As a security enthusiast, I wanted a tool that could help me systematically discover these hidden gems during testing. That’s where PyFuzz comes in.

I’ve always been fascinated by fuzzing—sending unexpected inputs to see how a system reacts. It’s like knocking on every door in a building to see which ones are unlocked. For APIs, this means trying out different endpoints to find ones that might not be documented or properly secured. I couldn’t find a lightweight tool that fit my needs, so I decided to build my own using Python.

How PyFuzz Works

PyFuzz is designed to be simple yet effective. Here’s the gist of what it does:

It reads a wordlist (I use apiroutes.txt) containing potential API routes like /users, /admin, or /login.
It sends GET requests to the target URL with each word appended (e.g., https://example.com/api/users).
It skips 404 Not Found responses to focus on valid endpoints.
If the response is a JSON, it tries to parse and display it for you.
It handles errors gracefully, so you don’t get stuck if something goes wrong.

I chose Python for this project because of its simplicity and the awesome requests library, which makes HTTP requests a breeze. The tool is lightweight, easy to use, and perfect for quick API enumeration during a security assessment.

Setting Up PyFuzz

Getting PyFuzz up and running is super straightforward. Here’s how you can try it out yourself:

Step 1: Clone the Repository

First, grab the code from GitHub:

git clone https://github.com/Kushal-39/PyFuzz----simple-python-api-fuzzer.git
cd PyFuzz----simple-python-api-fuzzer

Step 2: Install Dependencies

Make sure you have Python installed, then install the requests library:

pip install requests

Step 3: Run the Script

Now, fire up the script:

python fuzz.py

The script will prompt you for a base URL (e.g., https://example.com/api). Make sure there’s no trailing slash! It’ll then start checking each word in the apiroutes.txt wordlist for valid API responses.

What You’ll See in Action

When you run PyFuzz, it’ll give you output like this:

Give the url of the site, including the http(s)(NO TRAILING BACKSLASH)
> https://example.com/api
Total words to check: 50
[1/50] Checking: users
Response data: {"message": "Success", "users": [...]}
Status code: 200
Word: users
...

Pretty cool, right? It tells you which endpoints are live, their status codes, and even the response data if it’s JSON. This makes it easy to spot interesting endpoints that might need further investigation.

Challenges I Faced

Building PyFuzz wasn’t without its hurdles. One of the biggest challenges was dealing with rate limiting. Some APIs will block you if you send too many requests too quickly, returning a 429 Too Many Requests error. To get around this, I added some basic error handling to catch these responses and pause the script if needed. It’s not perfect, but it gets the job done for now.

Another tricky part was parsing JSON responses. Not all APIs return clean JSON—some responses might be malformed or in a different format altogether. I had to add some try-catch blocks to handle these cases gracefully without crashing the script.

What I Learned

This project taught me a ton about API security and the importance of fuzzing in penetration testing. It also reinforced how critical error handling is when building tools—APIs can be unpredictable, and your tool needs to be robust enough to handle that. Plus, I got more comfortable with Python’s requests library, which has been a lifesaver for other projects too.

A Word of Caution

Before you go fuzzing every API you find, a quick reminder: PyFuzz is meant for educational and ethical security research only. Always get explicit permission before testing any system. Unauthorized use can get you into serious legal trouble, and that’s the last thing I want for you!

What’s Next for PyFuzz?

PyFuzz is open-source under the GPL 3.0 License, and I’d love to see it grow. I’m already thinking about adding features like support for other HTTP methods (POST, PUT, etc.), multi-threading for faster fuzzing, and maybe even a way to export results to a file. If you’ve got ideas or want to contribute, check out the project on GitHub (link above) and let me know what you think!

Thanks for reading—I hope PyFuzz inspires you to dig deeper into API security. Stay curious, and happy hacking (ethically, of course)!