Individual skill
recon-techstack-fingerprinting
Passively identify a target's full technology stack — frontend framework, backend runtime, server, CMS, CDN/WAF, cloud, and versions — from public signals, then route to the right attack skills and CVEs. Load at the start of a web/API assessment, when choosing which techniques apply, or when "what is this built with / what CVEs match". Signals: a new domain in scope, unknown stack, "fingerprint", "what framework", version-to-CVE matching.
skills/recon/recon-techstack-fingerprinting/SKILL.md
Source commit ee0e0c50d2f6d941a14b73dd85a2b470d0ab976e
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
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- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 7, 2026
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Repository context
- Repository stars (not skill ratings)
- 27
- Repository last updated
- Oct 14, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- ai-agents Repository topic
- ai-security Repository topic
- application-security Repository topic
- blue-team Repository topic
- bug-bounty Repository topic
- claude-code Repository topic
- codex Repository topic
- detection-engineering Repository topic
- gemini Repository topic
- engineering Derived category
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# Tech-stack fingerprinting
## When it applies
The start of any web/API engagement, before you pick techniques. Knowing the stack turns a generic
"test everything" into a targeted plan: a Rails app routes to mass-assignment and deserialization,
a GraphQL backend to `api-graphql`, a WAF in front changes your `payloads-waf-bypass` approach, and
a pinned version turns into a CVE shortlist. It is passive OSINT — public signals only, no exploit.
## Why it works
Applications advertise themselves constantly: response headers, cookie names, HTML generator tags,
JS bundle names, error pages, TLS certs, DNS/CDN records, public repos, and job postings all leak
the stack. Cross-correlating several weak signals gives a high-confidence picture no single tool
produces — and confidence is what keeps you from scoring a CVE against a wrong guess.