# m4ll0k/BBTz

BBT - Bug Bounty Tools (examples💡)

Repository: https://github.com/m4ll0k/BBTz
Canonical: https://ross.abutalabs.com/products/bbtz
Language: Python
License Family: other
Last push: 2024-04-05T04:01:08+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2623, "days_push": 880, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1909, forks 467 (observed 2026-08-28T04:05:52.998217+00:00)

## What it is
A collection of bug bounty tools and example scripts written in Python by security researcher m4ll0k. It serves as a set of ideas and reference implementations for common bug bounty and security testing tasks.

## Use cases
- find tools for bug bounty hunting
- learn how to write security testing scripts in python
- collect recon and vulnerability hunting utilities
- get ideas for automating bug bounty workflows
- build custom pentesting scripts from examples

## When to choose
- you want example scripts and ideas for bug bounty automation
- you are learning offensive security tooling in Python
- you need a grab-bag of small security utilities to adapt

## When to avoid
- you need a production-grade, well-maintained tool with a license and support
- you want a single polished tool rather than a collection of examples
- you require guaranteed updates or security audits

## Facets
- artifact type: cli-tool
- maturity: experimental
- function: security, penetration-testing, osint, developer-tools
- domain: security, penetration-testing, developer-tools
- platform: windows, python, cli
- tags: bug-bounty, security-tools, collection, examples, recon, command-line, linux, macos

## Member repositories
- m4ll0k/BBTz (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:52.998217+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:11:07.333401+00:00, confidence not recorded.
  - readme: https://github.com/m4ll0k/BBTz (fetched 2026-08-28T04:05:52.998217+00:00, sha f7049152ee45)
- Data as of 2026-08-30T08:39:29.467469+00:00.