AI Vulnerability Scanner Assistant

A sophisticated security tool that leverages artificial intelligence to detect and analyze vulnerabilities in web applications and systems. This tool combines traditional scanning techniques with advanced AI-powered analysis to provide comprehensive security assessments and actionable recommendations.

🌟 Features

Core Scanning Capabilities

• Automated Vulnerability Scanning
  • Nmap integration for port and service detection
  • OWASP ZAP integration for web application security testing
  • SSL/TLS security analysis
  • Custom payload detection

AI-Powered Analysis

• Natural Language Processing

  • Intelligent command interpretation
  • Context-aware scanning configuration
  • Smart vulnerability classification

• Machine Learning Features

  • Payload analysis and classification
  • Risk scoring and prioritization
  • Pattern recognition for zero-day threats

Security Reports

• Detailed vulnerability assessments
• Risk-based prioritization
• Remediation recommendations
• Custom report generation (JSON, PDF, HTML)

System Features

• Automated updates via CI/CD pipeline
• Real-time monitoring and alerts
• Rate limiting and access control
• Backup and rollback capabilities

🚀 Installation

Prerequisites

• Python 3.8 or higher
• Docker (optional)
• Redis (optional, for rate limiting)
• PostgreSQL (optional, for data persistence)

Basic Installation

1. Clone the repository:

git clone https://github.com/Beelocks/vulnerability-scanner-ai.git
cd vulnerability-scanner-ai

2. Create and activate virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Install system dependencies:

# For Ubuntu/Debian
sudo apt-get update
sudo apt-get install nmap python3-dev libpq-dev

# For CentOS/RHEL
sudo yum update
sudo yum install nmap python3-devel postgresql-devel

5. Configure environment variables:

cp .env.example .env
# Edit .env with your configuration

Docker Installation

1. Build the Docker image:

docker build -t vuln-scanner .

2. Run the container:

docker run -d -p 5000:5000 --name vuln-scanner vuln-scanner

⚙️ Configuration

Environment Variables

# Core Settings
FLASK_ENV=development
SECRET_KEY=your-secure-secret-key
OPENAI_API_KEY=your-openai-key

# Database Settings
DATABASE_URL=postgresql://user:pass@localhost/dbname

# Security Settings
ALLOWED_HOSTS=localhost,example.com
RATE_LIMIT=100 per day
MAX_CONCURRENT_SCANS=5

# Integration Settings
GITHUB_TOKEN=your-github-token
SENTRY_DSN=your-sentry-dsn

Security Configuration

1. API Authentication:

# Generate API key
python -c "import secrets; print(secrets.token_urlsafe(32))"

2. SSL/TLS Configuration:

# Generate SSL certificate
openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout cert.key -out cert.pem

🔧 Usage

Basic Usage

1. Start the server:

python run.py

2. Access the API documentation:

http://localhost:5000/api/docs

API Examples

1. Initiate a scan:

curl -X POST "http://localhost:5000/api/scan" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "target": "example.com",
    "scan_type": "full"
  }'

2. Analyze a payload:

curl -X POST "http://localhost:5000/api/analyze-payload" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "your-payload-here",
    "content_type": "text"
  }'

3. Generate a report:

curl -X GET "http://localhost:5000/api/report/scan-id" \
  -H "X-API-Key: your-api-key"

🧪 Testing

Running Tests

# Run all tests
python -m pytest

# Run with coverage
python -m pytest --cov=app --cov-report=term-missing

# Run specific test category
python -m pytest tests/test_scanner.py

Test Coverage

• Unit tests for core functionality
• Integration tests for external services
• Security testing for authentication and authorization
• Performance testing for concurrent operations

🔒 Security Considerations

Best Practices

1. Always use a secure API key
2. Limit scan scope to authorized targets
3. Monitor system logs regularly
4. Keep dependencies updated
5. Use rate limiting to prevent abuse

Known Limitations

• Some scans may require root privileges
• Rate limiting may affect concurrent scans
• Large payloads may impact performance

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

Development Guidelines

• Follow PEP 8 style guide
• Write tests for new features
• Update documentation
• Use meaningful commit messages

📄 License

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

🙏 Acknowledgments

• OWASP ZAP team
• Nmap project
• OpenAI for AI capabilities
• All contributors and testers

📬 Contact

• Security Team: security@example.com
• Bug Reports: Create an issue in the GitHub repository
• Feature Requests: Use the GitHub discussions section

🔄 Updates and Maintenance

Version History

• v1.1.0 - Added AI-powered payload analysis
• v1.0.1 - Security patches and bug fixes
• v1.0.0 - Initial release

Roadmap

• Enhanced machine learning models
• Real-time threat detection
• Automated remediation suggestions
• Cloud integration features

In your GitHub repository

mkdir -p .github/workflows cp security-audit.yml .github/workflows/