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ffuf-web-fuzzing

ffuf-web-fuzzing

github.com/trailofbitsSecurity523100 installsUpdated 15w ago
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About ffuf-web-fuzzing

ffuf-web-fuzzing is a skill that guides authorized penetration testers in using ffuf to discover web paths, subdomains, API endpoints, and parameter behavior. It covers authenticated requests, scan controls, and JSON result analysis, with authorization and production rate limits as explicit constraints.

Key features

  1. 01Guidance for directory and file discovery
  2. 02Subdomain and API endpoint fuzzing
  3. 03Authenticated fuzzing with raw requests
  4. 04Wordlist, filtering, and rate-limit guidance
  5. 05Analysis of JSON results and anomalies
  6. 06Authorization checks before scanning

Use cases

  1. 01Discovering paths on an authorized web target
  2. 02Fuzzing API endpoints with a bearer token
  3. 03Testing authenticated requests using a captured session
  4. 04Reviewing saved results for unusual responses

Install

Copy the command and run it in a terminal.

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

After installing, type/ffuf-web-fuzzingin Claude Code to confirm it loaded.

Bundled filesreferences/request-templates.mdreferences/wordlists.md

Details

From the project README

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

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