Project overview
An AI-powered phishing detection platform developed for the TinyFish Hackathon to combat AI-driven scams and social engineering attacks.
Key capabilities
- Scans suspicious URLs within a sandboxed browser, protecting the user's device from malware while extracting live content for analysis.
- Uses AI-assisted analysis to detect phishing patterns such as homograph attacks and integrates with VirusTotal for domain reputation checks.
My contributions
- Developed a React frontend using Vite, Tailwind CSS, and shadcn/ui to stream live browser scan results via SSE (Server-Sent Events) from the TinyFish Agent API.
- Built a Python backend using FastAPI and Pydantic to orchestrate parallel calls for AI-assisted content analysis and VirusTotal threat intelligence.
- Designed and implemented the PostgreSQL database schema on Supabase to persist scan results, enabling instant history retrieval.
- Implemented an intelligent caching system with a smart Time-To-Live (TTL) to instantly deliver results for known sites, reducing API costs and staying within rate limits.
- Learning point : Utilizing Server-Sent Events (SSE) allows for efficient, real-time streaming of browser automation data to the frontend without the overhead of WebSockets.
Technology used
FastAPISupabaseReactVirusTotal APIAI-assisted URL analysisTypeScriptPython