🛡️ AutoPent.AI

AI-Powered Security Scanner with Professional Reporting

Transform URL security scanning with intelligent AI analysis and professional reporting

🚀 Live Demo • 📖 Documentation • 🔧 Installation • 🌐 Deploy

📋 Table of Contents

🎯 Overview
✨ Features
🏗️ Architecture
🚀 Quick Start
🔧 Installation
🌐 Deployment
📚 API Documentation
🎛️ Configuration
🤝 Contributing
📄 License

🎯 Overview

AutoPent.AI is a cutting-edge security assessment platform that combines automated vulnerability scanning with AI-powered analysis. It provides security professionals and developers with comprehensive security reports that are both technically accurate and business-friendly.

🎪 What Makes It Special?

🧠 AI-Powered Analysis: Leverages OpenAI GPT models for intelligent vulnerability assessment
📊 Professional Reports: Generates publication-ready PDF reports with executive summaries
⚡ Serverless Architecture: Built for scalability with Vercel's edge functions
🎯 Zero Configuration: Works out-of-the-box with minimal setup
🔒 Privacy-First: No data storage - scan results are temporary and secure

✨ Features

🔍 Comprehensive Security Scanning

HTTP Security Headers Analysis - Detects missing security headers (CSP, HSTS, X-Frame-Options)
SSL/TLS Configuration Assessment - Validates certificate security and protocol versions
Content Security Analysis - Identifies potential XSS vectors and inline scripts
Information Disclosure Detection - Finds exposed server information and debug data

🤖 AI-Enhanced Intelligence

Risk Prioritization - AI-powered vulnerability severity assessment
Fix Recommendations - Detailed, actionable remediation guidance
Business Impact Analysis - Translates technical findings into business risks
Contextual Insights - Industry-specific security recommendations

📄 Professional Reporting

Executive Summaries - Board-ready risk assessments
Technical Details - Comprehensive vulnerability breakdowns
Visual Risk Metrics - Charts and graphs for quick understanding
Compliance Mapping - OWASP Top 10 and industry standard alignment

🚀 Modern Architecture

Real-time Scanning - Live progress updates with WebSocket-like experience
Responsive Design - Mobile-friendly interface for on-the-go assessments
API-First Design - RESTful APIs for integration with existing workflows
Serverless Deployment - Automatic scaling and global edge distribution

🏗️ Architecture

graph TB
    A[User Interface] --> B[Flask API Gateway]
    B --> C[Security Scanner Engine]
    B --> D[AI Analysis Module]
    B --> E[Report Generator]

    C --> F[Header Analysis]
    C --> G[SSL/TLS Checks]
    C --> H[Content Scanner]

    D --> I[OpenAI GPT Integration]
    D --> J[Risk Assessment]
    D --> K[Remediation Engine]

    E --> L[PDF Generator]
    E --> M[Executive Summary]
    E --> N[Technical Report]

    B --> O[Vercel Edge Functions]
    O --> P[Global CDN]

Tech Stack

Layer	Technology	Purpose
Frontend	Vanilla JavaScript, Modern CSS	Responsive, fast UI
Backend	Flask, Python 3.12	RESTful API services
AI Engine	OpenAI GPT-4	Intelligent analysis
Reports	ReportLab	Professional PDF generation
Security	Custom Scanner Engine	Vulnerability detection
Deployment	Vercel, Serverless Functions	Global edge distribution

🚀 Quick Start

1. One-Click Deploy

2. Local Development

# Clone the repository
git clone https://github.com/your-username/AutoPent.AI.git
cd AutoPent.AI

# Install dependencies
pip install -r requirements.txt

# Configure environment (optional)
cp env.example .env
# Edit .env with your OpenAI API key

# Run the application
python run_web.py

🌐 Access at: http://localhost:5000

3. Docker Deployment

# Build and run with Docker
docker build -t autopent-ai .
docker run -p 5000:5000 -e OPENAI_API_KEY=your_key autopent-ai

🔧 Installation

Prerequisites

Python 3.12+ (3.9+ supported with legacy build)
Node.js 20.x or 22.x (for Vercel deployment)
OpenAI API Key (optional, for AI features)

Local Setup

Clone and Install

git clone https://github.com/your-username/AutoPent.AI.git
cd AutoPent.AI
pip install -r requirements.txt

Environment Configuration

cp env.example .env

Edit .env:

OPENAI_API_KEY=sk-your-openai-api-key-here
FLASK_ENV=development

Run Application
```
python run_web.py
```

Development Mode

# Run with auto-reload
python -m api.main

# Run tests
python -m pytest tests/

# Format code
black . && isort .

🌐 Deployment

Vercel (Recommended)

Connect Repository
- Fork this repository
- Connect to Vercel Dashboard
- Import your fork

Environment Variables

OPENAI_API_KEY=your_openai_api_key_here
PYTHONPATH=.

Deploy
- Push to main branch
- Automatic deployment via GitHub integration

Alternative Platforms

Railway Deployment

# Install Railway CLI
npm install -g @railway/cli

# Login and deploy
railway login
railway init
railway up

Heroku Deployment

# Install Heroku CLI and deploy
heroku create your-app-name
heroku config:set OPENAI_API_KEY=your_key
git push heroku main

AWS Lambda

# Use Serverless Framework
npm install -g serverless
serverless deploy

📚 API Documentation

Base URL

Production: https://your-app.vercel.app/api
Local: http://localhost:5000/api

Endpoints

Health Check

GET /api/health

Response:

{
  "status": "healthy",
  "timestamp": "2025-01-25T10:30:00Z",
  "version": "1.0.0",
  "environment": "production",
  "features": {
    "ai_analysis": true,
    "pdf_reports": true,
    "security_scanning": true
  }
}

Start Security Scan

POST /api/scan
Content-Type: application/json

Request:

{
  "target_url": "https://example.com",
  "include_ai": true
}

Response:

{
  "status": "success",
  "scan_id": "20250125_103000",
  "target_url": "https://example.com",
  "results": {
    "metadata": { "target_url": "https://example.com" },
    "summary": {
      "total_alerts": 5,
      "risk_counts": {
        "High": 1,
        "Medium": 2,
        "Low": 2
      }
    },
    "alerts": [...],
    "statistics": {...}
  },
  "ai_enabled": true,
  "ai_analyses_count": 5
}

Download Report

GET /api/download-report/{scan_id}

Response: PDF file download

Configuration

GET /api/config

Response:

{
  "ai_enabled": true,
  "max_scan_targets": 1,
  "supported_protocols": ["http", "https"],
  "features": {
    "security_headers": true,
    "ssl_analysis": true,
    "ai_analysis": true,
    "pdf_reports": true
  }
}

Response Codes

Code	Description
`200`	Success
`400`	Bad Request - Invalid parameters
`404`	Not Found - Resource doesn't exist
`500`	Internal Server Error

🎛️ Configuration

Environment Variables

Variable	Required	Default	Description
`OPENAI_API_KEY`	No	`""`	OpenAI API key for AI analysis
`FLASK_ENV`	No	`production`	Flask environment mode
`PYTHONPATH`	No	`"."`	Python module search path

Feature Toggles

# In config.py
class Config:
    # AI Features
    OPENAI_MODEL = "gpt-4"  # or "gpt-3.5-turbo"
    OPENAI_MAX_TOKENS = 1500
    OPENAI_TEMPERATURE = 0.3

    # Scanning Configuration
    MAX_SCAN_DEPTH = 5
    SCAN_TIMEOUT = 30  # seconds

    # Report Configuration
    REPORT_TITLE = "Security Assessment Report"
    REPORT_AUTHOR = "AutoPent.AI"

Advanced Configuration

Custom Scanner Settings

# Custom timeout and user agent
scanner_config = {
    "timeout": 10,
    "user_agent": "AutoPent.AI Security Scanner",
    "follow_redirects": True,
    "verify_ssl": False  # For testing
}

AI Model Customization

# Custom AI prompts and models
ai_config = {
    "model": "gpt-4",
    "temperature": 0.3,
    "max_tokens": 2000,
    "custom_prompts": {
        "vulnerability_analysis": "Custom prompt here..."
    }
}

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Quick Contribution Guide

Fork the repository
Create a feature branch
```
git checkout -b feature/amazing-feature
```
Make your changes
Add tests
```
python -m pytest tests/
```
Commit your changes
```
git commit -m "Add amazing feature"
```
Push to the branch
```
git push origin feature/amazing-feature
```
Open a Pull Request

Development Setup

# Install development dependencies
pip install -r requirements-dev.txt

# Install pre-commit hooks
pre-commit install

# Run linting
flake8 . && black . && isort .

# Run security checks
bandit -r .

Code Style

Python: PEP 8 compliance with Black formatting
JavaScript: ESLint with Airbnb configuration
Commits: Conventional Commits specification
Documentation: Google-style docstrings

🔒 Security

Responsible Disclosure

If you discover a security vulnerability, please send an email to security@autopent-ai.com. All security vulnerabilities will be promptly addressed.

Security Features

Input Validation: All user inputs are validated and sanitized
Rate Limiting: API endpoints are protected against abuse
Secure Headers: Security headers are implemented by default
No Data Persistence: Scan results are not stored permanently

📊 Performance

Benchmarks

Metric	Value	Notes
Scan Time	5-15 seconds	Depends on target response time
AI Analysis	10-30 seconds	Per vulnerability
Report Generation	2-5 seconds	PDF creation time
Memory Usage	<512MB	Peak during scan

Optimization Features

Serverless Cold Start: <2 seconds
Edge Caching: Static assets cached globally
Concurrent Scanning: Multiple vulnerability checks in parallel
Optimized AI Calls: Batch processing for efficiency

🎨 Roadmap

Upcoming Features

Multi-target Scanning - Scan multiple URLs simultaneously
Custom Rules Engine - User-defined security rules
Integration APIs - Slack, Teams, and webhook notifications
Historical Tracking - Trend analysis and improvement tracking
Team Collaboration - Multi-user workspaces
Custom Branding - White-label reporting options

Version History

v1.0.0 - Initial release with core scanning features
v1.1.0 - Added AI analysis and enhanced reporting
v1.2.0 - Vercel deployment optimization and performance improvements

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

OpenAI for providing the AI analysis capabilities
Vercel for the exceptional serverless platform
Flask community for the robust web framework
ReportLab for professional PDF generation
Security Community for vulnerability research and best practices

📞 Support

Getting Help

📖 Documentation: View Docs
💬 Community: Join Discord
🐛 Bug Reports: GitHub Issues
✉️ Email: support@autopent-ai.com

Enterprise Support

For enterprise features, custom integrations, or priority support, contact us at enterprise@autopent-ai.com.

Made with ❤️ for the security community

⭐ Star us on GitHub • 🐦 Follow on Twitter • 💼 LinkedIn

Name		Name	Last commit message	Last commit date
Latest commit History 21 Commits
ai_module		ai_module
api		api
parser		parser
public		public
report		report
scanner		scanner
.gitignore		.gitignore
README.md		README.md
config.py		config.py
env.example		env.example
requirements.txt		requirements.txt
run_web.py		run_web.py
vercel.json		vercel.json

uzaif-lab/AutoPent.AI

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🛡️ AutoPent.AI

📋 Table of Contents

🎯 Overview

🎪 What Makes It Special?

✨ Features

🔍 Comprehensive Security Scanning

🤖 AI-Enhanced Intelligence

📄 Professional Reporting

🚀 Modern Architecture

🏗️ Architecture

Tech Stack

🚀 Quick Start

1. One-Click Deploy

2. Local Development

3. Docker Deployment

🔧 Installation

Prerequisites

Local Setup

Development Mode

🌐 Deployment

Vercel (Recommended)

Alternative Platforms

📚 API Documentation

Base URL

Endpoints

Health Check

Start Security Scan

Download Report

Configuration

Response Codes

🎛️ Configuration

Environment Variables

Feature Toggles

Advanced Configuration

🤝 Contributing

Quick Contribution Guide

Development Setup

Code Style

🔒 Security

Responsible Disclosure

Security Features

📊 Performance

Benchmarks

Optimization Features

🎨 Roadmap

Upcoming Features

Version History

📄 License

🙏 Acknowledgments

📞 Support

Getting Help

Enterprise Support

About

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