stanleynull/autohunter — explained in plain English
Analysis updated 2026-05-18
Run continuous, automated vulnerability hunting against targets you have written authorization to test.
Discover candidate targets automatically through the FOFA internet asset search service.
Let an AI reviewer filter out false positives before you spend time reviewing findings.
Auto-fill education sector bug bounty reports by looking up which university a target belongs to.
| stanleynull/autohunter | 2417467487-hub/trend2video-pro | tencent-hunyuan/hy-embodied-rxbrain-1.0 | |
|---|---|---|---|
| Stars | 111 | 111 | 111 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | moderate | hard |
| Complexity | 4/5 | — | 5/5 |
| Audience | developer | vibe coder | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires Docker, a paid LLM API key, and ideally a FOFA key, first build also compiles the frontend and installs scanning tools, taking 5 to 15 minutes.
AutoHunter is a self hosted system that automates bug bounty style vulnerability hunting, built for security researchers who submit findings to programs like EduSRC, the education sector bug bounty platform in China. The idea is that you set the system running on a server around the clock, and instead of manually testing targets yourself, you spend your time reviewing the findings it surfaces before deciding what to submit. It works as a pipeline of cooperating pieces. A collector gathers candidate targets, either by querying the FOFA internet asset search service or from a manual list you provide, then checks whether each target is reachable and scores it. Each target is then handed to its own worker, which uses a large language model together with real security tools such as nmap, nuclei, sqlmap, and httpx to investigate it and attempt to find a genuine vulnerability, submitting anything it finds. A reviewer step then uses AI to filter out incomplete results or false positives before anything reaches you, and a dashboard lets you watch what every worker is doing in real time and make the final call on what gets submitted. For education sector targets, it can also automatically look up which university a given IP address or domain belongs to, to help fill out report details. The project explicitly states it is licensed under CC BY NC 4.0, meaning noncommercial use only, and that it must only be used against targets you have clear written authorization to test. It runs through Docker and Docker Compose on Linux, Windows with WSL2, or macOS, and setup is handled by an install script that walks you through supplying a language model API key, which is required, and a FOFA key, which is recommended for automatic target discovery. The backend is written in Python with FastAPI and SQLAlchemy, and the frontend uses Vue 3. The author describes this as a demo level project shared to help others, not a polished production tool.
A self hosted, LLM driven system that automates bug bounty vulnerability hunting across discovered targets, with human review before submission.
Mainly Python. The stack also includes Python, FastAPI, Vue 3.
Free to use, modify, and share for noncommercial purposes only, as long as you credit the original author.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
double-check against the repo, no cap.