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I've been working on HuntKit, an open-source, local-first framework designed to streamline and improve bug bounty reconnaissance and security testing.

The idea behind HuntKit is simple: instead of manually managing multiple security tools, workflows, outputs, and reports, bring everything together into one structured environment.

What makes HuntKit different?

• Automated, parallel and resumable reconnaissance pipelines.
• Integration with tools like Subfinder, httpx, Naabu, Nuclei, FFUF, Dalfox and more.
• Scope enforcement to prevent testing unauthorized assets.
• An intelligence engine that scores attack surfaces and prioritizes potential attack paths.
• Built-in vulnerability research playbooks covering 9 bug classes.
• Automated reporting in Markdown, HTML, JSON and PDF.
• Optional AI-assisted analysis using local Ollama models or cloud providers with explicit approval.
• Continuous monitoring, result comparison and notifications for new findings.
• A local-first architecture: engagement data stays on your machine by default.

HuntKit is built for security researchers and bug bounty hunters who want a more organized, efficient and reproducible testing workflow.

Tech stack: Python, CLI tooling, external security tools, AI integrations and Docker.

GitHub: github.com/Raresney/HuntKit

PyPI: pypi.org/project/huntkit/

I'm currently looking to connect with people working in cybersecurity, offensive security, AI-assisted security research and automation.

I'd especially appreciate feedback on the architecture, feature set and potential directions for future development.

Always intended for authorized security testing only.