A user-friendly, all-in-one platform for reconnaissance, mapping, and targeted attacks. Crafted for those who recognize that the best defense is a great offense.
Inspiration: PWNAPULT was inspired by the need for an efficient, AI-powered tool to help hackers map attack surfaces and identify vulnerabilities in a highly structured and user-friendly way.
What it does: It allows users to enter a target, select a scan type, run reconnaissance with Nmap, and use AI (Google Gemini) to generate a clear, actionable vulnerability report.
How we built it: We developed it using Django 5.0 on Python 3.10, with Waitress handling HTTP requests. Nmap scans run as subprocesses, and the Gemini API processes outputs for better readability.
Challenges we ran into: Our biggest challenge was integrating Google Gemini in a way that didn't leave critical vulnerabilities out and left us with a clean, easy to read report that could be understood by a person with any experience level.
Accomplishments that we're proud of: Successfully optimizing our firewall/IDS evasion techniques to create a stealthy, efficient scanning process.
What we learned: We gained deeper insights into Nmap scanning and and how to optimize its assessments.
What's next for PWNAPULT: Enhancing AI-driven attack recommendations and integrating additional reconnaissance tools for deeper vulnerability analysis.
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