Expert guidance for ffuf web fuzzing during penetration testing, including authenticated fuzzing with raw requests, auto-calibration, and result analysis
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name
ffuf-web-fuzzing
description
Expert guidance for ffuf web fuzzing during penetration testing, including authenticated fuzzing with raw requests, auto-calibration, and result analysis
You are fuzzing web targets with ffuf during authorized security testing or penetration testing.
The task involves content discovery, subdomain enumeration, parameter fuzzing, or authenticated request fuzzing.
You need guidance on wordlists, filtering, calibration, and interpreting ffuf results efficiently.
Overview
FFUF is a fast web fuzzer written in Go, designed for discovering hidden content, directories, files, subdomains, and testing for vulnerabilities during penetration testing. It's significantly faster than traditional tools like dirb or dirbuster.
Installation
# Using Go
go install github.com/ffuf/ffuf/v2@latest
# Using Homebrew (macOS)
brew install ffuf
# Binary download# Download from: https://github.com/ffuf/ffuf/releases/latest
Core Concepts
The FUZZ Keyword
The FUZZ keyword is used as a placeholder that gets replaced with entries from your wordlist. You can place it anywhere:
URLs: https://target.com/FUZZ
Headers: -H "Host: FUZZ"
POST data: -d "username=admin&password=FUZZ"
Multiple locations with custom keywords: -w wordlist.txt:CUSTOM then use CUSTOM instead of FUZZ
Multi-wordlist Modes
clusterbomb: Tests all combinations (default) - cartesian product
pitchfork: Iterates through wordlists in parallel (1-to-1 matching)
sniper: Tests one position at a time (for multiple FUZZ positions)
-mc: Match status codes (default: 200-299,301,302,307,401,403,405,500)
-ml: Match line count
-mr: Match regex
-ms: Match response size
-mt: Match response time (e.g., >100 or <100 milliseconds)
-mw: Match word count
Filters (Exclude Results)
-fc: Filter status codes (e.g., -fc 404,403,401)
-fl: Filter line count
-fr: Filter regex (e.g., -fr "error")
-fs: Filter response size (e.g., -fs 42,4242)
-ft: Filter response time
-fw: Filter word count
Auto-Calibration (USE BY DEFAULT!)
CRITICAL: Always use -ac unless you have a specific reason not to. This is especially important when having Claude analyze results, as it dramatically reduces noise and false positives.
# Auto-calibration - ALWAYS USE THIS
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -ac
# Per-host auto-calibration (useful for multiple hosts)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -ach
# Custom auto-calibration string (for specific patterns)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -acc "404NotFound"
Why -ac is essential:
Automatically detects and filters repetitive false positive responses
Removes noise from dynamic websites with random content
Makes results analysis much easier for both humans and Claude
Prevents thousands of identical 404/403 responses from cluttering output
Adapts to the target's specific behavior
When Claude analyzes your ffuf results, -ac is MANDATORY - without it, Claude will waste time sifting through thousands of false positives instead of finding the interesting anomalies.
Rate Limiting and Timing
Rate Control
# Limit to 2 requests per second (stealth mode)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -rate 2
# Add delay between requests (0.1 to 2 seconds random)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -p 0.1-2.0
# Set number of concurrent threads (default: 40)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -t 10
Time Limits
# Maximum total execution time (60 seconds)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -maxtime 60
# Maximum time per job (useful with recursion)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -maxtime-job 60 -recursion
Output Options
Output Formats
# JSON output
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -o results.json
# HTML output
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -of html -o results.html
# CSV output
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -of csv -o results.csv
# All formats
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -of all -o results
# Silent mode (no progress, only results)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -s
# Pipe to file with tee
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -s | tee results.txt
Advanced Techniques
Using Raw HTTP Requests (Critical for Authenticated Fuzzing)
This is one of the most powerful features of ffuf, especially for authenticated requests with complex headers, cookies, or tokens.
Workflow:
Capture a full authenticated request (from Burp Suite, browser DevTools, etc.)
Save it to a file (e.g., req.txt)
Replace the value you want to fuzz with the FUZZ keyword
Use the --request flag
# From a file containing raw HTTP request
ffuf --request req.txt -w /path/to/wordlist.txt -ac
Don't struggle with command-line flags for complex auth. Capture the full request and use --request:
# 1. Capture authenticated request from Burp/DevTools# 2. Save to req.txt with FUZZ keyword in place# 3. Run with -ac
ffuf --request req.txt -w wordlist.txt -ac -o results.json
ffuf -w wordlist.txt -X POST -d "param=FUZZ" -u https://target.com/endpoint
With Extensions
Add -e .php,.html,.txt
Filter Status
Add -fc 404,403
Filter Size
Add -fs 1234
Rate Limit
Add -rate 2
Save Output
Add -o results.json
Verbose
Add -c -v
Recursion
Add -recursion -recursion-depth 2
Through Proxy
Add -x http://127.0.0.1:8080
Additional Resources
This skill includes supplementary materials in the resources/ directory:
Resource Files
WORDLISTS.md: Comprehensive guide to SecLists wordlists, recommended lists for different scenarios, file extensions, and quick reference patterns
REQUEST_TEMPLATES.md: Pre-built req.txt templates for common authentication scenarios (JWT, OAuth, session cookies, API keys, etc.) with usage examples
Helper Script
ffuf_helper.py: Python script to assist with:
Analyzing ffuf JSON results for anomalies and interesting findings
Creating req.txt template files from command-line arguments
Generating number-based wordlists for IDOR testing
