BishopFox/eyeballer
Convolutional neural network for analyzing pentest screenshots observed · 2026-08-28
Health v2 · maintenance only
55/100
- Activity 71
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2737
- days_rel: n/a
- days_push: 178
- n_releases_24m: 0
Adoption not part of the score
1290 stars · 148 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Eyeballer is a convolutional neural network tool that classifies screenshots of web hosts taken during large-scope penetration tests. It labels images as old-looking sites, login pages, webapps, custom 404s, or parked domains to help pentesters prioritize interesting targets.
Use cases
- classify pentest screenshots of web hosts
- find vulnerable-looking old websites at scale
- identify login pages for credential attacks
- filter out custom 404 pages and parked domains
- triage EyeWitness or GoWitness screenshot output
When to choose
- you have thousands of web screenshots from a large-scope pentest and need to prioritize targets
- you want ML-based classification instead of brittle heuristics for login page detection
When to avoid
- you need real-time or API-based analysis rather than batch screenshot classification
- your scope is small enough to review screenshots manually
Facets
cli-tool · maturity active
machine-learning image-processing computer-vision security security machine-learning penetration-testing python cli pentesting screenshot-analysis cnn tensorflow bishopfox linux macos
1 source
- readme: https://github.com/BishopFox/eyeballer · fetched 2026-08-28 · 7ec8095d5a97
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| BishopFox/eyeballer | main | 55 |
For agents
markdown · JSON · MCP: product_card(name="BishopFox/eyeballer")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem