external/trailofbits-skills-curated/plugins/ffuf-web-fuzzing/skills/ffuf-web-fuzzing/SKILL.md
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.
npx skillsauth add seikaikyo/dash-skills ffuf-web-fuzzingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Guidance for using ffuf (Fuzz Faster U Fool) effectively during authorized penetration testing.
ffuf must be installed: brew install ffuf (macOS) or go install github.com/ffuf/ffuf/v2@latest
-ac is mandatory. Without it, results are buried in false positives and analysis is wasted effort.-t 200 triggers WAFs, gets you blocked, and may crash staging environments. Start with -t 10 -rate 2 for production targets.--request req.txt is simpler and more reliable than chaining -H and -b flags. Capture once, fuzz many times.-ac (auto-calibration) unless you have a specific, documented reason not to-o results.json for later analysis-rate and -t flags--request for auth -- raw request files beat command-line header chains# 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
-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
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
ffuf -w /opt/SecLists/Discovery/DNS/subdomains-top1million-5000.txt \
-u https://FUZZ.target.com -ac -c -v -o results.json
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
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 requires real credentials that the agent cannot obtain independently. When the user asks for authenticated fuzzing:
req.txt) with auth headers already includedreq.txt format)req.txtffuf --request req.txt -w <(seq 1 10000) -ac -mc 200 -o idor_results.json
| Environment | Flags | Notes |
|-------------|-------|-------|
| Production (stealth) | -rate 2 -t 10 | Avoid WAF triggers |
| Production (normal) | -rate 10 -t 20 | Balanced |
| Staging/Dev | -rate 50 -t 40 | Faster |
| Local/Lab | No limit, -t 100 | Maximum speed |
Save output as JSON (-o results.json), then read the file and focus on:
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.
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.