Ffuf Web Fuzzing

FFUF (Fuzz Faster U Fool) Skill

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/ffuf-web-fuzzing from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

FFUF (Fuzz Faster U Fool) Skill

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Concepts, Common Use Cases, Filtering and Matching, Rate Limiting and Timing, Output Options, Advanced Techniques.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 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.
• Use when the request clearly matches the imported source intent: Expert guidance for ffuf web fuzzing during penetration testing, including authenticated fuzzing with raw requests, auto-calibration, and result analysis.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. bash # 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
2. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
3. Read the overview and provenance files before loading any copied upstream support files.
4. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
5. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
6. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
7. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.

Imported Workflow Notes

Imported: 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

Imported: 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.

Imported: 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)

Examples

Example 1: Ask for the upstream workflow directly

Use @ffuf-web-fuzzing to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @ffuf-web-fuzzing against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @ffuf-web-fuzzing for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @ffuf-web-fuzzing using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Directory discovery: SecLists Discovery/Web-Content (raft-large-directories.txt, directory-list-2.3-medium.txt)
• Subdomains: SecLists Discovery/DNS (subdomains-top1million-5000.txt)
• Parameters: SecLists Discovery/Web-Content (burp-parameter-names.txt)
• Usernames: SecLists Usernames
• Passwords: SecLists Passwords
• Source: https://github.com/danielmiessler/SecLists
• Check the default response first to identify common response sizes, status codes, or patterns

Imported Operating Notes

Imported: Best Practices

1. ALWAYS Use Auto-Calibration

Use -ac by default for every scan. This is non-negotiable for productive pentesting:

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

2. Use Raw Requests for Authentication

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

3. Use Appropriate Wordlists

• Directory discovery: SecLists Discovery/Web-Content (raft-large-directories.txt, directory-list-2.3-medium.txt)
• Subdomains: SecLists Discovery/DNS (subdomains-top1million-5000.txt)
• Parameters: SecLists Discovery/Web-Content (burp-parameter-names.txt)
• Usernames: SecLists Usernames
• Passwords: SecLists Passwords
• Source: https://github.com/danielmiessler/SecLists

3. Rate Limiting for Stealth

Use -rate to avoid triggering WAF/IDS or overwhelming the server:

ffuf -w wordlist.txt -u https://target.com/FUZZ -rate 2 -t 10

4. Filter Strategically

• Check the default response first to identify common response sizes, status codes, or patterns
• Use -fs to filter by size or -fc to filter by status code
• Combine filters: -fc 403,404 -fs 1234

5. Save Results Appropriately

Always save results to a file for later analysis:

ffuf -w wordlist.txt -u https://target.com/FUZZ -o results.json -of json

6. Use Interactive Mode

Press ENTER during execution to drop into interactive mode where you can:

• Adjust filters on the fly
• Save current results
• Restart the scan
• Manage the queue

7. Recursion Depth

Be careful with recursion depth to avoid getting stuck in infinite loops or overwhelming the server:

ffuf -w wordlist.txt -u https://target.com/FUZZ -recursion -recursion-depth 2 -maxtime-job 120

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/ffuf-web-fuzzing, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: Troubleshooting

Too Many False Positives

• Use -ac for auto-calibration
• Check default response and filter by size with -fs
• Use regex filtering with -fr

Too Slow

• Increase threads: -t 100
• Reduce wordlist size
• Use -ignore-body if you don't need response content

Getting Blocked

• Reduce rate: -rate 2
• Add delays: -p 0.5-1.5
• Reduce threads: -t 10
• Randomize User-Agent
• Use proxy rotation

Missing Results

• Check if you're filtering too aggressively
• Use -mc all to see all responses
• Disable auto-calibration temporarily
• Use verbose mode -v to see what's happening

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Resources

• Official GitHub: https://github.com/ffuf/ffuf
• Wiki: https://github.com/ffuf/ffuf/wiki
• Codingo's Guide: https://codingo.io/tools/ffuf/bounty/2020/09/17/everything-you-need-to-know-about-ffuf.html
• Practice Lab: http://ffuf.me
• SecLists Wordlists: https://github.com/danielmiessler/SecLists

Imported: Quick Reference Card

Imported: 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

Helper Script Usage:

# Analyze results to find interesting anomalies
python3 ffuf_helper.py analyze results.json

# Create authenticated request template
python3 ffuf_helper.py create-req -o req.txt -m POST -u "https://api.target.com/users" \
    -H "Authorization: Bearer TOKEN" -d '{"action":"FUZZ"}'

# Generate IDOR testing wordlist
python3 ffuf_helper.py wordlist -o ids.txt -t numbers -s 1 -e 10000

When to use resources:

• Users need wordlist recommendations → Reference WORDLISTS.md
• Users need help with authenticated requests → Reference REQUEST_TEMPLATES.md
• Users want to analyze results → Use ffuf_helper.py analyze
• Users need to generate req.txt → Use ffuf_helper.py create-req
• Users need number ranges for IDOR → Use ffuf_helper.py wordlist

Imported: Common Use Cases

1. Directory and File Discovery

# Basic directory fuzzing
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ

# With file extensions
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -e .php,.html,.txt,.pdf

# Colored and verbose output
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -c -v

# With recursion (finds nested directories)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -recursion -recursion-depth 2

2. Subdomain Enumeration

# Virtual host discovery
ffuf -w /path/to/subdomains.txt -u https://target.com -H "Host: FUZZ.target.com" -fs 4242

# Note: -fs 4242 filters out responses of size 4242 (adjust based on default response size)

3. Parameter Fuzzing

# GET parameter names
ffuf -w /path/to/params.txt -u https://target.com/script.php?FUZZ=test_value -fs 4242

# GET parameter values
ffuf -w /path/to/values.txt -u https://target.com/script.php?id=FUZZ -fc 401

# Multiple parameters
ffuf -w params.txt:PARAM -w values.txt:VAL -u https://target.com/?PARAM=VAL -mode clusterbomb

4. POST Data Fuzzing

# Basic POST fuzzing
ffuf -w /path/to/passwords.txt -X POST -d "username=admin&password=FUZZ" -u https://target.com/login.php -fc 401

# JSON POST data
ffuf -w entries.txt -u https://target.com/api -X POST -H "Content-Type: application/json" -d '{"name": "FUZZ", "key": "value"}' -fr "error"

# Fuzzing multiple POST fields
ffuf -w users.txt:USER -w passes.txt:PASS -X POST -d "username=USER&password=PASS" -u https://target.com/login -mode pitchfork

5. Header Fuzzing

# Custom headers
ffuf -w /path/to/wordlist.txt -u https://target.com -H "X-Custom-Header: FUZZ"

# Multiple headers
ffuf -w /path/to/wordlist.txt -u https://target.com -H "User-Agent: FUZZ" -H "X-Forwarded-For: 127.0.0.1"

Imported: Filtering and Matching

Matchers (Include Results)

• -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.

Imported: 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

Imported: 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

Imported: 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:

1. Capture a full authenticated request (from Burp Suite, browser DevTools, etc.)
2. Save it to a file (e.g., req.txt)
3. Replace the value you want to fuzz with the FUZZ keyword
4. Use the --request flag

# From a file containing raw HTTP request
ffuf --request req.txt -w /path/to/wordlist.txt -ac

Example req.txt file:

POST /api/v1/users/FUZZ HTTP/1.1
Host: target.com
User-Agent: Mozilla/5.0
Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...
Cookie: session=abc123xyz; csrftoken=def456
Content-Type: application/json
Content-Length: 27

{"action":"view","id":"1"}

Use Cases:

• Fuzzing authenticated endpoints with complex auth headers
• Testing API endpoints with JWT tokens
• Fuzzing with CSRF tokens, session cookies, and custom headers
• Testing endpoints that require specific User-Agents or Accept headers
• POST/PUT/DELETE requests with authentication

Pro Tips:

• You can place FUZZ in multiple locations: URL path, headers, body
• Use -request-proto https if needed (default is https)
• Always use -ac to filter out authenticated "not found" or error responses
• Great for IDOR testing: fuzz user IDs, document IDs, etc. in authenticated contexts

# Common authenticated fuzzing patterns
ffuf --request req.txt -w user_ids.txt -ac -mc 200 -o results.json

# With multiple FUZZ positions using custom keywords
ffuf --request req.txt -w endpoints.txt:ENDPOINT -w ids.txt:ID -mode pitchfork -ac

Proxy Usage

# HTTP proxy (useful for Burp Suite)
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -x http://127.0.0.1:8080

# SOCKS5 proxy
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -x socks5://127.0.0.1:1080

# Replay matched requests through proxy
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -replay-proxy http://127.0.0.1:8080

Cookie and Authentication

# Using cookies
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -b "sessionid=abc123; token=xyz789"

# Client certificate authentication
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -cc client.crt -ck client.key

Encoding

# URL encoding
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -enc 'FUZZ:urlencode'

# Multiple encodings
ffuf -w /path/to/wordlist.txt -u https://target.com/FUZZ -enc 'FUZZ:urlencode b64encode'

Testing for Vulnerabilities

# SQL injection testing
ffuf -w sqli_payloads.txt -u https://target.com/page.php?id=FUZZ -fs 1234

# XSS testing
ffuf -w xss_payloads.txt -u https://target.com/search?q=FUZZ -mr "<script>"

# Command injection
ffuf -w cmdi_payloads.txt -u https://target.com/execute?cmd=FUZZ -fr "error"

Batch Processing Multiple Targets

# Process multiple URLs
cat targets.txt | xargs -I@ sh -c 'ffuf -w wordlist.txt -u @/FUZZ -ac'

# Loop through multiple targets with results
for url in $(cat targets.txt); do
    ffuf -w wordlist.txt -u $url/FUZZ -ac -o "results_$(echo $url | md5sum | cut -d' ' -f1).json"
done

Imported: Common Patterns and One-Liners

Quick Directory Scan

ffuf -w ~/wordlists/common.txt -u https://target.com/FUZZ -mc 200,301,302,403 -ac -c -v

Comprehensive Scan with Extensions

ffuf -w ~/wordlists/raft-large-directories.txt -u https://target.com/FUZZ -e .php,.html,.txt,.bak,.old -ac -c -v -o results.json

Authenticated Fuzzing (Raw Request)

# 1. Save your authenticated request to req.txt with FUZZ keyword
# 2. Run:
ffuf --request req.txt -w ~/wordlists/api-endpoints.txt -ac -o results.json -of json

API Endpoint Discovery

ffuf -w ~/wordlists/api-endpoints.txt -u https://api.target.com/v1/FUZZ -H "Authorization: Bearer TOKEN" -mc 200,201 -ac -c

Subdomain Discovery with Auto-Calibration

ffuf -w ~/wordlists/subdomains-top5000.txt -u https://FUZZ.target.com -ac -c -v

POST Login Brute Force

ffuf -w ~/wordlists/passwords.txt -X POST -d "username=admin&password=FUZZ" -u https://target.com/login -fc 401 -rate 5 -ac

IDOR Testing with Auth

# Use req.txt with authenticated headers and FUZZ in the ID parameter
ffuf --request req.txt -w numbers.txt -ac -mc 200 -fw 100-200

Imported: Configuration File

Create ~/.config/ffuf/ffufrc for default settings:

[http]
headers = ["User-Agent: Mozilla/5.0"]
timeout = 10

[general]
colors = true
threads = 40

[matcher]
status = "200-299,301,302,307,401,403,405,500"

Imported: Notes for Claude

When helping users with ffuf:

1. ALWAYS include -ac in every command - This is mandatory for productive pentesting and result analysis
2. When users mention authenticated fuzzing or provide auth tokens/cookies:
   • Suggest creating a req.txt file with the full HTTP request
   • Show them how to insert FUZZ where they want to fuzz
   • Use ffuf --request req.txt -w wordlist.txt -ac
3. Always recommend starting with -ac for auto-calibration
4. Suggest appropriate wordlists from SecLists based on the task
5. Remind users to use rate limiting (-rate) for production targets
6. Encourage saving output to files for documentation: -o results.json
7. Suggest filtering strategies based on initial reconnaissance
8. Always use the FUZZ keyword (case-sensitive)
9. Consider stealth: lower threads, rate limiting, and delays for sensitive targets
10. For pentesting reports, use -of html or -of csv for client-friendly formats
11. When analyzing ffuf results for users:
    • Assume they used -ac (if not, results will be too noisy)
    • Focus on anomalies: different status codes, response sizes, timing
    • Look for interesting endpoints: admin, api, backup, config, .git, etc.
    • Flag potential vulnerabilities: error messages, stack traces, version info
    • Suggest follow-up fuzzing on interesting findings

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.