TinyPhish

Project Overview

An AI-powered phishing detection platform developed for the TinyFish Hackathon to combat AI-driven scams and social engineering attacks.

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.

Tech Stack

FastAPISupabaseReactVirusTotal APIAI-assisted URL analysisTypeScriptPython

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.

Project Links

Project Overview

An AI-powered phishing detection platform developed for the TinyFish Hackathon to combat AI-driven scams and social engineering attacks.

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.

Tech Stack

FastAPISupabaseReactVirusTotal APIAI-assisted URL analysisTypeScriptPython

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.

Project Links