MCPInstall

ffuf-web-fuzzing

Expert guidance for ffuf web fuzzing during authorized penetration testing. Covers directory discovery, subdomain enumeration, parameter fuzzing, authenticated fuzzing with raw requests, auto-calibration, and result analysis. Use when running ffuf scans, analyzing ffuf output, or building fuzzing strategies for web targets.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/trailofbits/skills-curated --skill ffuf-web-fuzzing
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads ffuf-web-fuzzing automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

FFUF Web Fuzzing

Guidance for using ffuf (Fuzz Faster U Fool) effectively during authorized penetration testing.

Prerequisites

ffuf must be installed: brew install ffuf (macOS) or go install github.com/ffuf/ffuf/v2@latest

When to Use

  • Running directory, file, or subdomain discovery against web targets
  • Fuzzing API endpoints, parameters, or POST data
  • Authenticated fuzzing with raw HTTP requests
  • Analyzing ffuf JSON output for anomalies and interesting findings
  • Building fuzzing strategies (wordlist selection, filtering, rate limiting)
  • IDOR testing with authenticated sessions

When NOT to Use

  • Target system is not in scope or authorization is unclear
  • Passive reconnaissance is more appropriate (use OSINT tools instead)
  • The target is a production system and rate limiting hasn't been configured
  • You need a full vulnerability scanner (use Burp Suite, Nuclei, etc.)
  • Testing for logic flaws that require multi-step interaction

Rationalizations to Reject

  • "Auto-calibration is optional" -- -ac is mandatory. Without it, results are buried in false positives and analysis is wasted effort.
  • "More threads = faster results" -- Hammering a target with -t 200 triggers WAFs, gets you blocked, and may crash staging environments. Start with -t 10 -rate 2 for production targets.
  • "I'll filter later" -- Set up filtering before the scan. Running a 220k wordlist without filters and then trying to grep through the noise is backwards.
  • "The default wordlist is fine" -- Wordlist selection is the most important decision. A generic wordlist misses technology-specific paths. See references/wordlists.md.
  • "Raw requests are too much work" -- For authenticated fuzzing, --request req.txt is simpler and more reliable than chaining -H and -b flags. Capture once, fuzz many times.

Critical Rules

  1. Always use -ac (auto-calibration) unless you have a specific, documented reason not to
  2. Always save output with -o results.json for later analysis
  3. Rate limit production targets with -rate and -t flags
  4. Use --request for auth -- raw request files beat command-line header chains
  5. Confirm authorization first -- before running any scan, verify the user has written permission for the target. Ask if unclear.

Core Concepts

The FUZZ Keyword

# In URL path
ffuf -w wordlist.txt -u https://target.com/FUZZ -ac

# In headers
ffuf -w wordlist.txt -u https://target.com -H "Host: FUZZ.target.com" -ac

# In POST body
ffuf -w wordlist.txt -X POST -d "user=admin&pass=FUZZ" -u https://target.com/login -ac

# Multiple positions with custom keywords
ffuf -w endpoints.txt:EP -w ids.txt:ID -u https://target.com/EP/ID -mode pitchfork -ac

Auto-Calibration

-ac automatically detects and filters repetitive false-positive responses. It adapts to the target's specific behavior and removes noise from dynamic content.

ffuf -w wordlist.txt -u https://target.com/FUZZ -ac        # Standard
ffuf -w wordlist.txt -u https://target.com/FUZZ -ach       # Per-host (multi-host scans)
ffuf -w wordlist.txt -u https://target.com/FUZZ -acc "404" # Custom calibration string

Common Patterns

Directory Discovery

ffuf -w /opt/SecLists/Discovery/Web-Content/raft-large-directories.txt \
     -u https://target.com/FUZZ -e .php,.html,.txt,.bak \
     -ac -c -v -o results.json

Subdomain Enumeration

ffuf -w /opt/SecLists/Discovery/DNS/subdomains-top1million-5000.txt \
     -u https://FUZZ.target.com -ac -c -v -o results.json

API Endpoint Discovery

ffuf -w /opt/SecLists/Discovery/Web-Content/api/api-endpoints.txt \
     -u https://api.target.com/v1/FUZZ \
     -H "Authorization: Bearer YOUR_TOKEN_HERE" -mc 200,201 -ac -c

Authenticated Fuzzing with Raw Requests

Capture a full authenticated request, save to req.txt, insert FUZZ:

POST /api/v1/users/FUZZ HTTP/1.1
Host: target.com
Authorization: Bearer YOUR_TOKEN_HERE
Cookie: session=YOUR_SESSION_ID
Content-Type: application/json

{"action":"view","id":"1"}
ffuf --request req.txt -w wordlist.txt -ac -o results.json

See references/request-templates.md for pre-built templates covering bearer tokens, session cookies, API keys, and GraphQL.

Authenticated Fuzzing: Agent Workflow

Authenticated fuzzing requires real credentials that the agent cannot obtain independently. When the user asks for authenticated fuzzing:

  1. Ask the user to provide ONE of:
    • A raw HTTP request file (req.txt) with auth headers already included
    • A curl command from browser DevTools (convert it to req.txt format)
    • Individual credentials (Bearer token, session cookie, API key)
  2. If given a curl command, convert it to raw HTTP request format and write to req.txt
  3. If given individual credentials, use a template from references/request-templates.md and substitute real values
  4. Never fabricate or guess authentication tokens

IDOR Testing

ffuf --request req.txt -w <(seq 1 10000) -ac -mc 200 -o idor_results.json

Rate Limiting

EnvironmentFlagsNotes
Production (stealth)-rate 2 -t 10Avoid WAF triggers
Production (normal)-rate 10 -t 20Balanced
Staging/Dev-rate 50 -t 40Faster
Local/LabNo limit, -t 100Maximum speed

Analyzing Results

Save output as JSON (-o results.json), then read the file and focus on:

  • Anomalous status codes -- anything other than the baseline 404/403
  • Size outliers -- responses significantly larger or smaller than average
  • Interesting keywords in URLs -- admin, api, backup, config, .git, .env
  • Timing anomalies -- slow responses may indicate SQL injection or heavy processing
  • Follow-up targets -- interesting findings warrant deeper fuzzing

Use -fs to filter by response size and -fc to filter by status code when auto-calibration isn't sufficient. Run ffuf -h for the full list of match/filter flags.

References

  • Wordlist selection guide -- recommended SecLists by scenario
  • Authenticated request templates -- pre-built req.txt for bearer tokens, cookies, API keys
  • ffuf official docs
  • SecLists

FAQ

What does the ffuf-web-fuzzing skill do?

Expert guidance for ffuf web fuzzing during authorized penetration testing. Covers directory discovery, subdomain enumeration, parameter fuzzing, authenticated fuzzing with raw requests, auto-calibration, and result analysis. Use when running ffuf scans, analyzing ffuf output, or building fuzzing strategies for web targets.

How do I install ffuf-web-fuzzing?

Run npx skills add https://github.com/trailofbits/skills-curated --skill ffuf-web-fuzzing in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ffuf-web-fuzzing/ (personal) or .claude/skills/ffuf-web-fuzzing/ (project).

Which AI agents can use ffuf-web-fuzzing?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is ffuf-web-fuzzing safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains ffuf-web-fuzzing?

It is published by trailofbits in trailofbits/skills-curated. It has 99 recorded installs.

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