dast-ffuf

ffuf - Fast Web Fuzzer

Overview

ffuf is a fast web fuzzer written in Go designed for discovering hidden resources, testing parameters, and performing comprehensive web application reconnaissance. It uses the FUZZ keyword as a placeholder for wordlist entries and supports advanced filtering, multiple fuzzing modes, and recursive scanning for thorough security assessments.

Installation

# Using Go
go install github.com/ffuf/ffuf/v2@latest

# Using package managers
# Debian/Ubuntu
apt install ffuf

# macOS
brew install ffuf

# Or download pre-compiled binary from GitHub releases

Quick Start

Basic directory fuzzing:

# Directory discovery
ffuf -u https://example.com/FUZZ -w /usr/share/wordlists/dirb/common.txt

# File discovery with extension
ffuf -u https://example.com/FUZZ -w wordlist.txt -e .php,.html,.txt

# Virtual host discovery
ffuf -u https://example.com -H "Host: FUZZ.example.com" -w subdomains.txt

Core Workflows

Workflow 1: Directory and File Enumeration

For discovering hidden resources on web applications:

1. Start with common directory wordlist:

``bash ffuf -u https://target.com/FUZZ \ -w /usr/share/seclists/Discovery/Web-Content/common.txt \ -mc 200,204,301,302,307,401,403 \ -o results.json ``

1. Review discovered directories (focus on 200, 403 status codes)
2. Enumerate files in discovered directories:

``bash ffuf -u https://target.com/admin/FUZZ \ -w /usr/share/seclists/Discovery/Web-Content/raft-small-files.txt \ -e .php,.bak,.txt,.zip \ -mc all -fc 404 ``

1. Use recursive mode for deep enumeration:

``bash ffuf -u https://target.com/FUZZ \ -w wordlist.txt \ -recursion -recursion-depth 2 \ -e .php,.html \ -v ``

1. Document findings and test discovered endpoints

Workflow 2: Parameter Fuzzing (GET/POST)

Progress: [ ] 1. Identify target endpoint for parameter testing [ ] 2. Fuzz GET parameter names to discover hidden parameters [ ] 3. Fuzz parameter values for injection vulnerabilities [ ] 4. Test POST parameters with JSON/form data [ ] 5. Apply appropriate filters to reduce false positives [ ] 6. Analyze responses for anomalies and vulnerabilities [ ] 7. Validate findings manually [ ] 8. Document vulnerable parameters and payloads

Work through each step systematically. Check off completed items.

GET Parameter Name Fuzzing:

ffuf -u https://target.com/api?FUZZ=test \
  -w /usr/share/seclists/Discovery/Web-Content/burp-parameter-names.txt \
  -fs 0  # Filter out empty responses

GET Parameter Value Fuzzing:

ffuf -u https://target.com/api?id=FUZZ \
  -w payloads.txt \
  -mc all

POST Data Fuzzing:

# Form data
ffuf -u https://target.com/login \
  -X POST \
  -d "username=admin&password=FUZZ" \
  -w passwords.txt \
  -H "Content-Type: application/x-www-form-urlencoded"

# JSON data
ffuf -u https://target.com/api/login \
  -X POST \
  -d '{"username":"admin","password":"FUZZ"}' \
  -w passwords.txt \
  -H "Content-Type: application/json"

Workflow 3: Virtual Host and Subdomain Discovery

For identifying virtual hosts and subdomains:

1. Prepare subdomain wordlist (or use SecLists)
2. Run vhost fuzzing:

``bash ffuf -u https://target.com \ -H "Host: FUZZ.target.com" \ -w /usr/share/seclists/Discovery/DNS/subdomains-top1million-5000.txt \ -fs 0 # Filter by response size to identify valid vhosts ``

1. Filter results by comparing response sizes/words
2. Verify discovered vhosts manually
3. Enumerate directories on each vhost
4. Document vhost configurations and exposed services

Workflow 4: Authentication Endpoint Fuzzing

For testing login forms and authentication mechanisms:

1. Identify authentication endpoint
2. Fuzz usernames:

``bash ffuf -u https://target.com/login \ -X POST \ -d "username=FUZZ&password=test123" \ -w usernames.txt \ -H "Content-Type: application/x-www-form-urlencoded" \ -mr "Invalid password|Incorrect password" # Match responses indicating valid user ``

1. For identified users, fuzz passwords:

``bash ffuf -u https://target.com/login \ -X POST \ -d "username=admin&password=FUZZ" \ -w /usr/share/seclists/Passwords/Common-Credentials/10-million-password-list-top-1000.txt \ -H "Content-Type: application/x-www-form-urlencoded" \ -fc 401,403 # Filter failed attempts ``

1. Use clusterbomb mode for combined username/password fuzzing:

``bash ffuf -u https://target.com/login \ -X POST \ -d "username=FUZZ1&password=FUZZ2" \ -w usernames.txt:FUZZ1 \ -w passwords.txt:FUZZ2 \ -mode clusterbomb ``

Workflow 5: Backup and Sensitive File Discovery

For finding exposed backup files and sensitive data:

1. Create wordlist of common backup patterns
2. Fuzz for backup files:

``bash ffuf -u https://target.com/FUZZ \ -w backup-files.txt \ -e .bak,.backup,.old,.zip,.tar.gz,.sql,.7z \ -mc 200 \ -o backup-files.json ``

1. Test common sensitive file locations:

``bash ffuf -u https://target.com/FUZZ \ -w /usr/share/seclists/Discovery/Web-Content/sensitive-files.txt \ -mc 200,403 ``

1. Download and analyze discovered files
2. Report findings with severity classification

Fuzzing Modes

ffuf supports multiple fuzzing modes for different attack scenarios:

Clusterbomb Mode - Cartesian product of all wordlists (default):

ffuf -u https://target.com/FUZZ1/FUZZ2 \
  -w dirs.txt:FUZZ1 \
  -w files.txt:FUZZ2 \
  -mode clusterbomb

Tests every combination: dir1/file1, dir1/file2, dir2/file1, dir2/file2

Pitchfork Mode - Parallel iteration of wordlists:

ffuf -u https://target.com/login \
  -X POST \
  -d "username=FUZZ1&password=FUZZ2" \
  -w users.txt:FUZZ1 \
  -w passwords.txt:FUZZ2 \
  -mode pitchfork

Tests pairs: user1/pass1, user2/pass2 (stops at shortest wordlist)

Sniper Mode - One wordlist, multiple positions:

ffuf -u https://target.com/FUZZ \
  -w wordlist.txt \
  -mode sniper

Standard single-wordlist fuzzing.

Filtering and Matching

Effective filtering is crucial for reducing noise:

Match Filters (only show matching):

• -mc 200,301 - Match HTTP status codes
• -ms 1234 - Match response size
• -mw 100 - Match word count
• -ml 50 - Match line count
• -mr "success|admin" - Match regex pattern in response

Filter Options (exclude matching):

• -fc 404,403 - Filter status codes
• -fs 0,1234 - Filter response sizes
• -fw 0 - Filter word count
• -fl 0 - Filter line count
• -fr "error|not found" - Filter regex pattern

Auto-Calibration:

# Automatically filter baseline responses
ffuf -u https://target.com/FUZZ -w wordlist.txt -ac

Common Patterns

Pattern 1: API Endpoint Discovery

Discover REST API endpoints:

# Enumerate API paths
ffuf -u https://api.target.com/v1/FUZZ \
  -w /usr/share/seclists/Discovery/Web-Content/api/api-endpoints.txt \
  -mc 200,201,401,403 \
  -o api-endpoints.json

# Fuzz API versions
ffuf -u https://api.target.com/FUZZ/users \
  -w <(seq 1 10 | sed 's/^/v/') \
  -mc 200

Pattern 2: Extension Fuzzing

Test multiple file extensions:

# Brute-force extensions on known files
ffuf -u https://target.com/admin.FUZZ \
  -w /usr/share/seclists/Discovery/Web-Content/web-extensions.txt \
  -mc 200

# Or use -e flag for multiple extensions
ffuf -u https://target.com/FUZZ \
  -w filenames.txt \
  -e .php,.asp,.aspx,.jsp,.html,.bak,.txt

Pattern 3: Rate-Limited Fuzzing

Respect rate limits and avoid detection:

# Add delay between requests
ffuf -u https://target.com/FUZZ \
  -w wordlist.txt \
  -p 0.5-1.0  # Random delay 0.5-1.0 seconds

# Limit concurrent requests
ffuf -u https://target.com/FUZZ \
  -w wordlist.txt \
  -t 5  # Only 5 concurrent threads

Pattern 4: Custom Header Fuzzing

Fuzz HTTP headers for security mi