Web Fuzzing Security Specialist

## Curation Note Web fuzzing remains one of the most effective techniques for discovering hidden attack surfaces. This skill gained traction in the bug bounty community where AI agents assist in reconnaissance phases. The combination of ffuf (one of the fastest web fuzzers available) with AI-driven analysis creates a powerful workflow for identifying directories, files, subdomains, and API endpoints that manual testing would miss. ## Prerequisites ```bash go install github.com/ffuf/ffuf/v2@latest brew install ffuf # macOS apt install ffuf # Debian/Ubuntu ffuf -V ``` **Required Wordlists:** ```bash git clone https://github.com/danielmiessler/SecLists.git ~/SecLists ``` ## Safety & Ethics > **WARNING**: Only perform fuzzing on systems you have explicit authorization to test. Before fuzzing: 1. Verify written authorization exists 2. Confirm testing scope and boundaries 3. Check for rate limiting requirements 4. Document all testing activities ## Core Fuzzing Operations ### Directory Discovery ```bash ffuf -u https://target.com/FUZZ -w ~/SecLists/Discovery/Web-Content/directory-list-2.3-medium.txt ffuf -u https://target.com/FUZZ -w wordlist.txt -fs 4242 ffuf -u https://target.com/FUZZ -w wordlist.txt -fc 404,403 ffuf -u https://target.com/FUZZ -w wordlist.txt -mc 200,301,302 ``` ### File Discovery ```bash ffuf -u https://target.com/FUZZ -w wordlist.txt -e .php,.bak,.old,.txt,.html ffuf -u https://target.com/FUZZ -w wordlist.txt -recursion -recursion-depth 2 ``` ### Subdomain Enumeration ```bash ffuf -u https://FUZZ.target.com -w ~/SecLists/Discovery/DNS/subdomains-top1million-5000.txt ffuf -u https://target.com -H "Host: FUZZ.target.com" -w subdomains.txt -fs 4242 ``` ### API Endpoint Discovery ```bash ffuf -u https://api.target.com/vFUZZ/users -w versions.txt ffuf -u https://target.com/api/users -X FUZZ -w ~/SecLists/Fuzzing/http-request-methods.txt ffuf -u "https://target.com/api/search?FUZZ=test" -w ~/SecLists/Discovery/Web-Content/burp-parameter-names.txt ``` ### POST Data Fuzzing ```bash ffuf -u https://target.com/api/login \ -X POST \ -H "Content-Type: application/json" \ -d '{"username":"admin","password":"FUZZ"}' \ -w passwords.txt ffuf -u https://target.com/login \ -X POST \ -d "username=admin&password=FUZZ" \ -w passwords.txt ``` ## Advanced Techniques ### Rate Limiting ```bash ffuf -u https://target.com/FUZZ -w wordlist.txt -rate 50 ffuf -u https://target.com/FUZZ -w wordlist.txt -p 0.1 ``` ### Authentication ```bash ffuf -u https://target.com/admin/FUZZ -w wordlist.txt -b "session=abc123" ffuf -u https://target.com/api/FUZZ -w wordlist.txt -H "Authorization: Bearer token123" ``` ### Output Formats ```bash ffuf -u https://target.com/FUZZ -w wordlist.txt -o results.json -of json ffuf -u https://target.com/FUZZ -w wordlist.txt -o results.csv -of csv ffuf -u https://target.com/FUZZ -w wordlist.txt -o results.html -of html ``` ### Multi-Position Fuzzing ```bash ffuf -u https://target.com/FUZZ1/FUZZ2 \ -w usernames.txt:FUZZ1 \ -w ids.txt:FUZZ2 \ -mode clusterbomb ``` ## Intelligent Analysis Workflow ### Phase 1: Initial Reconnaissance ```bash ffuf -u https://target.com/FUZZ \ -w ~/SecLists/Discovery/Web-Content/common.txt \ -mc 200,301,302,401,403 \ -o recon_common.json -of json ``` ### Phase 2: Deep Discovery Based on initial findings, expand search: ```bash ffuf -u https://target.com/api/FUZZ \ -w ~/SecLists/Discovery/Web-Content/api/api-endpoints.txt \ -mc 200 \ -o api_endpoints.json -of json ``` ### Phase 3: Parameter Analysis For discovered endpoints, identify parameters: ```bash ffuf -u "https://target.com/api/users?FUZZ=1" \ -w ~/SecLists/Discovery/Web-Content/burp-parameter-names.txt \ -fs baseline_size \ -o params.json -of json ``` ### Phase 4: Vulnerability Testing Test discovered parameters for common vulnerabilities: ```bash ffuf -u "https://target.com/search?q=FUZZ" \ -w ~/SecLists/Fuzzing/SQLi/quick-SQLi.txt \ -fs baseline_size ffuf -u "https://target.com/search?q=FUZZ" \ -w ~/SecLists/Fuzzing/XSS/xss-payload-list.txt \ -fs baseline_size ``` ## Results Interpretation ### Response Codes | Code | Meaning | Action | | ------- | -------------------- | -------------------------------- | | 200 | Found and accessible | Investigate content | | 301/302 | Redirect exists | Follow and analyze destination | | 401 | Auth required | Note as protected resource | | 403 | Forbidden | Potential exists, check bypasses | | 500 | Server error | Possible vulnerability | ### Filtering Strategy 1. Run initial scan without filters 2. Identify common response size for "not found" pages 3. Filter that size for subsequent scans 4. Use `-fc` to exclude common error codes ## Best Practices 1. **Start slow** - Begin with low rate limits, increase gradually 2. **Monitor responses** - Watch for blocks or WAF triggers 3. **Use targeted wordlists** - Technology-specific lists work better 4. **Document everything** - Save all output for analysis 5. **Verify findings** - Manually confirm interesting results 6. **Respect scope** - Stay within authorized boundaries 7. **Check robots.txt** - Often reveals hidden paths ## Related Resources - [ffuf GitHub](https://github.com/ffuf/ffuf) - Official repository - [SecLists](https://github.com/danielmiessler/SecLists) - Wordlist collection - [OWASP Testing Guide](https://owasp.org/www-project-web-security-testing-guide/) - Security testing methodology