AI Vulnerability Pattern Generator

app interface
offline pdf report continue
app interface
dashboard
dashboard continue
vulnerability check results offline
offline pdf report
vulnerability check results Google Gemini AI online
Google Gemini AI online pdf report
code analyzing
supabase app database
supabase app database
Google Gemini AI online pdf report

Inspiration

We wanted to create a tool that empowers developers and security enthusiasts to quickly identify security vulnerabilities in their code. Inspired by how time-consuming manual code review can be, we envisioned a solution that combines AI capabilities with local static analysis to save developers valuable time and reduce human error.

What it does

AI Vulnerability Pattern Generator analyzes code snippets for security vulnerabilities both offline (without internet connection) and online (with AI-powered insights). It detects common issues such as SQL injection, command injection, XSS, weak cryptography, and more, and generates detailed reports. It also offers the ability to export both AI and offline analysis as PDFs.

How we built it

We built the app using Bolt.new for rapid prototyping, Supabase for storing user data and reports, and integrated Google Gemini AI for advanced analysis when connected to the internet. We developed custom local detection patterns using TypeScript, allowing offline usage. We also created a polished interface and generated final voiceovers and videos using ElevenLabs.

Challenges we ran into

We faced challenges merging offline detection with the AI analysis flow. We also spent considerable time designing the PDF export to handle long text, wrap properly, and paginate well. Integrating Supabase.

Accomplishments that we're proud of

We built a fully working solution combining AI analysis and offline pattern detection. We polished the user interface, ensured smooth export of reports, and made the tool extensible for future languages and vulnerability types. The project truly saves time and supports both security professionals and developers.

What we learned

We deepened our skills in AI integration with Gemini, Bolt.new rapid app development, Supabase RLS policies, TypeScript patterns, and dynamic PDF generation.

What's next for AI Vulnerability Pattern Generator

We plan to extend language support beyond PHP and Python, integrate continuous updates to detection patterns, and improve AI-driven risk scoring. We also want to add charts and graphs in PDF reports, and build an online portal to share vulnerability cases securely.