AI Bug Bounty Automation System

Sahyadri College Of Engineering and Management

A comprehensive AI bug bounty system integrated with inbuilt Headless Kali Container, AI-powered chat assistance. The system uses a distributed architecture to provide intelligent command suggestions, automated tool execution, and report generation on the basis of different vulnerabilities that are present in websites.

📋 Table of Contents

• System Overview
• Architecture
• Components
• Setup & Installation

🔍 System Overview

The AI Bug Bounty Automation System is designed to streamline and automate security testing processes by combining:

• AI-powered command suggestions
• Automated tool execution
• Intelligent result analysis
• reporting

🏗 Architecture

The system consists of several key layers

Tech Stacks included

• Frontend
  • React.js for an intuitive web dashboard
  • Typescript for type safety
• Backend
  • Python, Fast API for backend servers
  • Node.js, Express.js
  • Dockerized Kali Headless OS
  • Langchain

🔧 Components

1. User Interface

• Web Dashboard: Modern web interface for:
  • Tool configuration
  • Report generation
• ** Real Time Kali Terminal**: Command-line interface for direct tool interaction

2. AI Engine

• Command Suggestion Engine
  • Intelligent tool selection
  • Context-aware suggestions
  • Vulnerability assessment

3. Tool Execution Layer

Dockerized security tools including:

• Nikto: Web server scanner
• Subfinder: Subdomain discovery
• Amass: Attack surface mapping
• SQLMap: SQL injection testing
• Nmap: Network scanning
• Nuclei: Vulnerability scanner
• Other kali tools..

4. Result Processing & Storage

• Report Generator: Creates detailed security reports
• Log Processor: Centralizes and analyzes tool outputs

📥 Setup & Installation

Prerequisites

• Python 3.8+
• Docker & Docker Compose

Local Setup

1. Clone the Repository

git clone 

2. Install Dependencies

pip install -r requirements.txt

3. Start Docker Containers

./start-kali.sh

3. Start all servers

./main.sh

AI Engine Settings

ai_engine:
  model: "codellama/CodeLlama-7b-Instruct-hf"

🔒 Security Considerations

• All tools run in isolated Docker containers
• Rate limiting implemented for API endpoints
• Authentication required for sensitive operations
• Results encrypted at rest

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