aiskillstore/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:

   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
   

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

   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
   

4. Use recursive mode for deep enumeration:

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

5. 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:

   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
   

3. Filter results by comparing response sizes/words
4. Verify discovered vhosts manually
5. Enumerate directories on each vhost
6. 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:

   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
   

3. For identified users, fuzz passwords:

   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
   

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

   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:

   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
   

3. Test common sensitive file locations:

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

4. Download and analyze discovered files
5. 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 misconfigurations:

# Fuzz custom headers
ffuf -u https://target.com/admin \
  -w headers.txt:HEADER \
  -H "HEADER: true" \
  -mc all

# Fuzz header values
ffuf -u https://target.com/admin \
  -H "X-Forwarded-For: FUZZ" \
  -w /usr/share/seclists/Fuzzing/IPs.txt \
  -mc 200

Pattern 5: Cookie Fuzzing

Test cookie-based authentication and session management:

# Fuzz cookie values
ffuf -u https://target.com/dashboard \
  -b "session=FUZZ" \
  -w session-tokens.txt \
  -mc 200

# Fuzz cookie names
ffuf -u https://target.com/admin \
  -b "FUZZ=admin" \
  -w cookie-names.txt

Output Formats

Save results in multiple formats:

# JSON output (recommended for parsing)
ffuf -u https://target.com/FUZZ -w wordlist.txt -o results.json -of json

# CSV output
ffuf -u https://target.com/FUZZ -w wordlist.txt -o results.csv -of csv

# HTML report
ffuf -u https://target.com/FUZZ -w wordlist.txt -o results.html -of html

# All formats
ffuf -u https://target.com/FUZZ -w wordlist.txt -o results -of all

Security Considerations

• Sensitive Data Handling: Discovered files may contain credentials, API keys, or PII. Handle findings securely and report responsibly
• Access Control: Only fuzz applications with proper authorization. Obtain written permission before testing third-party systems
• Audit Logging: Log all fuzzing activities including targets, wordlists used, and findings for compliance and audit trails
• Compliance: Ensure fuzzing activities comply with bug bounty program rules, penetration testing agreements, and legal requirements
• Safe Defaults: Use reasonable rate limits to avoid DoS conditions. Start with small wordlists before scaling up

Integration Points

Reconnaissance Workflow

1. Subdomain enumeration (amass, subfinder)
2. Port scanning (nmap)
3. Service identification
4. ffuf directory/file enumeration
5. Content discovery and analysis
6. Vulnerability scanning

CI/CD Security Testing

Integrate ffuf into automated security pipelines:

# CI/CD script
#!/bin/bash
set -e

# Run directory enumeration
ffuf -u https://staging.example.com/FUZZ \
  -w /wordlists/common.txt \
  -mc 200,403 \
  -o ffuf-results.json \
  -of json

# Parse results and fail if sensitive files found
if grep -q "/.git/\|/backup/" ffuf-results.json; then
  echo "ERROR: Sensitive files exposed!"
  exit 1
fi

Integration with Burp Suite

1. Use Burp to identify target endpoints
2. Export interesting requests
3. Convert to ffuf commands for automated fuzzing
4. Import ffuf results back to Burp for manual testing

Troubleshooting

Issue: Too Many False Positives

Solution: Use auto-calibration or manual filtering:

# Auto-calibration
ffuf -u https://target.com/FUZZ -w wordlist.txt -ac

# Manual filtering by size
ffuf -u https://target.com/FUZZ -w wordlist.txt -fs 1234,5678

Issue: Rate Limiting or Blocking

Solution: Reduce concurrency and add delays:

ffuf -u https://target.com/FUZZ \
  -w wordlist.txt \
  -t 1 \
  -p 2.0 \
  -H "User-Agent: Mozilla/5.0..."

Issue: Large Wordlist Takes Too Long

Solution: Start with smaller, targeted wordlists:

# Use top 1000 instead of full list
head -1000 /usr/share/seclists/Discovery/Web-Content/directory-list-2.3-medium.txt > small.txt
ffuf -u https://target.com/FUZZ -w small.txt

Issue: Missing Discovered Content

Solution: Test with multiple extensions and match codes:

ffuf -u https://target.com/FUZZ \
  -w wordlist.txt \
  -e .php,.html,.txt,.asp,.aspx,.jsp \
  -mc all \
  -fc 404

OWASP Testing Integration

Map ffuf usage to OWASP Testing Guide categories:

• WSTG-CONF-04: Review Old Backup and Unreferenced Files
• WSTG-CONF-05: Enumerate Infrastructure and Application Admin Interfaces
• WSTG-CONF-06: Test HTTP Methods
• WSTG-IDENT-01: Test Role Definitions (directory enumeration)
• WSTG-ATHZ-01: Test Directory Traversal/File Include
• WSTG-INPVAL-01: Test for Reflected Cross-site Scripting
• WSTG-INPVAL-02: Test for Stored Cross-site Scripting

References

• ffuf GitHub Repository
• SecLists Wordlists
• OWASP Web Security Testing Guide