transilienceai OSINT Tech-Stack Identification
Scope
Recover technology signals from public, non-target sources:
- Code repositories — public GitHub/GitLab orgs, language stats, dependency files (
package.json,requirements.txt,Gemfile,go.mod,pom.xml,composer.json), CI configs,Dockerfiles. - Career signals — career page, ATS platform (Greenhouse, Lever, Workday, Ashby, iCIMS, Taleo, etc.), job-description tech requirements.
- Web archives — Wayback Machine CDX queries to detect historical stack and migrations.
These provide indirect but valuable corroboration; weight lower than direct technical signals.
Signals (input)
- Public repos: org metadata, language map, dependency files, CI/CD configs, Dockerfile FROM lines
- Career page URL + ATS URL pattern + job-description text
- Wayback CDX snapshots (every 6-12 months over 5y)
Inferences (output)
- Languages and frameworks (with version ranges from dep files)
- CI/CD platform and pipeline maturity
- Container base images and orchestration manifests
- ATS-derived hiring profile (startup vs enterprise)
- Tech-mention frequency (Core Stack ≥50% / Common 25-50% / Occasional <25%)
- Historical stack timeline + migration events (jQuery → React, WordPress → Next.js, on-prem → cloud)
Techniques
See reference/patterns.md.
When to use
- Phase 2 of tech-stack OSINT (corroborating signal layer)
- Validating a hypothesis from direct signals (e.g. confirm Node.js via
package.json) - Detecting recent migrations (current stack different from historical)
- Inferring internal stack when public surface is heavily obscured (Cloudflare-fronted, headless API, etc.)