Trinity-SYT-SECURITY

Trinity-SYT-SECURITY / BountyFlow

Public

LLM-powered security relationship mapping & automated exploits

16
1
100% credibility
Found Apr 08, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
JavaScript
AI Summary

BountyFlow is a user-friendly web platform that helps penetration testers organize projects, run tools, visualize security relationships through interactive graphs, leverage AI for insights, and automatically generate professional reports.

How It Works

1
📰 Discover BountyFlow

You hear about this friendly tool that keeps all your security checks organized in one spot.

2
🚀 Set it up easily

Follow a few simple steps to get your personal testing workspace ready on your computer.

3
📝 Start your first project

Name your testing job, add a quick description, and note what's okay to check.

4
🎯 Add targets to test

List websites or servers you want to examine and quickly check if they respond.

5
🔧 Run helpful tools

Pick scanners or checkers from the list, aim them at your targets, and watch live results.

6
🧠 Watch connections form

See your security map light up, revealing how targets, issues, and users link together.

📊 Create your report

Hit generate to get a polished summary of everything you found, ready to share.

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Star Growth

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AI-Generated Review

What is BountyFlow?

BountyFlow is a full-stack penetration testing platform that unifies scattered security tools into one dashboard for managing projects, targets, findings, and automated tool runs. It visualizes security relationships via interactive knowledge graphs powered by Neo4j, chains exploits into attack workflows, and generates reports with AI assistance from Gemini, OpenAI, or Claude. Built with JavaScript on Next.js frontend and Python FastAPI backend, it handles everything from asset discovery to forensics logging in a single deployable app.

Why is it gaining traction?

Its LLM-powered autonomous agents stand out by auto-extracting relationships from tool outputs, recommending next steps, and building attack chains without manual graphing. Developers love the one-command Docker startup with Neo4j/Redis, auto-detecting AI providers via .env keys, and real-time WebSocket updates for live tool execution. For building LLM-powered applications on GitHub, it skips boilerplate by normalizing external tool data into graphs.

Who should use this?

Solo bug bounty hunters juggling Nmap/Burp outputs, pentest teams mapping complex networks, or security researchers automating exploit chains. Ideal for those tired of Excel for findings and static diagrams for attack paths, especially if you're evaluating LLM-powered security mapping in JavaScript-heavy stacks.

Verdict

Try it for prototyping automated security workflows—docs and setup are solid despite 16 stars and 1.0% credibility score signaling early maturity. Pair with production DB swaps for real engagements, but expect tweaks for scale.

(198 words)

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