Why is auto-calibration (-ac) recommended for this skill?

Auto-calibration is essential because it automatically filters out repetitive false positives and noise from dynamic web responses, allowing Claude to focus on analyzing meaningful anomalies.

Can I use this skill for authenticated fuzzing?

Yes, the skill provides detailed instructions on using raw HTTP request files to maintain complex headers, session cookies, and JWT tokens during the fuzzing process.

How does FFUF handle multiple wordlists?

FFUF supports multiple wordlists through modes like 'clusterbomb' (Cartesian product) and 'pitchfork' (parallel iteration), which are useful for testing multiple parameters simultaneously.

FFUF (Fuzz Faster U Fool) is a high-performance web fuzzer written in Go, used to discover hidden files, directories, and vulnerabilities by substituting keywords from wordlists into HTTP requests.

FFUF Web Fuzzing Skill

Name: FFUF Web Fuzzing Skill
Author: Activer007

byActiver007

0•

보안 및 테스팅

Streamlines web fuzzing workflows with expert guidance on FFUF for discovering hidden content, subdomains, and vulnerabilities during penetration testing.

This skill empowers Claude to act as a security testing specialist, providing precise configurations for FFUF (Fuzz Faster U Fool). It covers advanced techniques such as authenticated fuzzing using raw HTTP requests, multi-wordlist strategies like clusterbomb and pitchfork, and critical auto-calibration to minimize noise. Whether you are performing directory discovery, parameter fuzzing, or subdomain enumeration, this skill ensures results are analyzed efficiently and common false positives are filtered out, delivering actionable security insights for professional audits.

주요 기능

01Multi-wordlist execution modes for complex parameter fuzzing

02Guidance on rate limiting and stealth techniques for WAF evasion

030 GitHub stars

04Automated noise reduction and result filtering via auto-calibration

05Comprehensive directory, file, and subdomain discovery strategies

06Advanced authenticated fuzzing using raw HTTP request files

사용 사례

01Discovering hidden API endpoints and administrative directories during security audits

02Identifying IDOR and authentication bypass vulnerabilities through parameter fuzzing

03Mapping attack surfaces via subdomain enumeration and virtual host discovery

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add activer007/ordinary-claude-skills ffuf-skill

For use in Claude.ai and ChatGPT

Download Skill