CVE-2023-38831 Scanner

Overview

The CVE-2023-38831 Scanner is an advanced cybersecurity tool designed to detect and analyze the presence of the CVE-2023-38831 vulnerability in WinRAR installations. This project implements a multi-layered approach to vulnerability detection, incorporating file integrity checks, process memory scanning, network traffic analysis, and sandbox execution.

Features

• Vulnerability Detection: Scans WinRAR installations for the CVE-2023-38831 vulnerability.
• File Integrity Checking: Verifies the integrity of WinRAR executable files.
• Process Memory Scanning: Analyzes the memory of running WinRAR processes for suspicious patterns.
• Network Traffic Analysis: Monitors network traffic for potential exploitation attempts.
• Sandbox Execution: Safely executes and analyzes WinRAR in a controlled environment.
• Web Interface: Provides a user-friendly web-based interface for initiating scans and viewing results.
• Database Integration: Stores and retrieves scan results for historical analysis.
• Visualization: Presents scan results through interactive charts and tables.

Technical Architecture

Core Components

1. Scanner Module (src/scanner.py): Orchestrates the scanning process, integrating all detection methods.
2. Integrity Checker (src/integrity.py): Implements file hash verification for WinRAR executables.
3. Memory Scanner (src/memory_scanner.py): Utilizes psutil for process memory analysis.
4. Network Analyzer (src/network_analyzer.py): Leverages scapy for network packet inspection.
5. Sandbox Environment (src/sandbox.py): Creates an isolated environment for safe execution and analysis.
6. Database Handler (src/database.py): Manages SQLite database operations for result storage and retrieval.
7. Web Interface (src/web_interface.py): Implements a Flask-based web server for user interaction.

Key Technologies

• Python 3.8+: Core programming language
• SQLite: Lightweight database for scan result storage
• Flask: Web framework for the user interface
• Scapy: Network packet manipulation and analysis
• psutil: Cross-platform process and system monitoring
• Chart.js: JavaScript library for result visualization

Installation

1. Clone the repository:
   • git clone https://github.com/yezzfusl/cve_2023_38831_scanner.git
   • cd cve_2023_38831_scanner
2. Create and activate a virtual environment:
   • python -m venv venv
   • source venv/bin/activate # On Windows, use venv\Scripts\activate
3. Install dependencies:
   • pip install -r requirements.txt
4. Set up the database:
   • python -c "from src.database import Database; Database().create_table()"

Usage

Command Line Interface

• Run the scanner from the command line:
  • python -m src.cli

Web Interface

• Start the web server:
  • python -m src.web_interface

Access the web interface at http://localhost:5000.

Configuration

• Database settings: Modify src/database.py to change the database file location or switch to a different database system.
• Scan parameters: Adjust threshold values and detection patterns in respective module files (e.g., src/memory_scanner.py, src/network_analyzer.py).

Testing

• Run the test suite:
  • pytest tests/

Security Considerations

• This tool should only be used on systems you own or have explicit permission to test.
• The sandbox environment provides an additional layer of security, but caution should still be exercised when scanning potentially malicious files.
• Network traffic analysis may capture sensitive data. Ensure compliance with relevant privacy laws and regulations.

Performance Optimization

• The memory scanner utilizes efficient memory mapping techniques to minimize resource usage.
• Database queries are optimized for performance, with proper indexing on frequently accessed columns.
• The web interface implements asynchronous scanning to prevent UI blocking during long-running scans.

Extending the Project

• Additional Vulnerability Checks: Implement new detection methods in separate modules and integrate them into the scanner.py module.
• API Development: Extend the web interface to provide a RESTful API for integration with other security tools.
• Machine Learning Integration: Incorporate ML models for anomaly detection in process behavior or network traffic patterns.

Contributing

1. Fork the repository.
2. Create a new branch for your feature: git checkout -b feature-name
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin feature-name
5. Submit a pull request.

License

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

Acknowledgments

• CVE-2023-38831 details: MITRE CVE Database
• WinRAR: Official Website

Disclaimer

This tool is for educational and professional use only. The authors are not responsible for any misuse or damage caused by this program. Always ensure you have permission before scanning systems or networks you do not own.