Ffuf Web Fuzzing logo

Ffuf Web Fuzzing

Organization
trailofbits
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.

Overview

Publishertrailofbits
Repositoryskills-curated
Skill nameffuf-web-fuzzing
Stars
513
Forks
35
Bundled files
2
LicenseCC-BY-SA-4.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by trailofbits on GitHub. Read the source before you install it.

Installation

Install the Ffuf Web Fuzzing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/trailofbits/skills-curated/tree/main/plugins/ffuf-web-fuzzing/skills/ffuf-web-fuzzing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/trailofbits/skills-curated.git /tmp/skills-curated
mkdir -p .claude/skills
cp -r /tmp/skills-curated/plugins/ffuf-web-fuzzing/skills/ffuf-web-fuzzing .claude/skills/ffuf-web-fuzzing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ffuf Web Fuzzing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ffuf Web Fuzzing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ffuf Web Fuzzing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

bash
# 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.

bash
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

bash
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

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

API Endpoint Discovery

bash
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:

http
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"}
bash
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

bash
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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ffuf Web Fuzzing AI 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.

Why use Ffuf Web Fuzzing on TypingMind?

Because you install it once and use it with any model. Ffuf Web Fuzzing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ffuf Web Fuzzing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trailofbits/skills-curated/tree/main/plugins/ffuf-web-fuzzing/skills/ffuf-web-fuzzing. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ffuf Web Fuzzing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ffuf Web Fuzzing?

As many as you like. As long as a model supports skills, you can use Ffuf Web Fuzzing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ffuf Web Fuzzing AI skill free?

Yes. It is published on GitHub by trailofbits under the CC-BY-SA-4.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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