Python Detector

📁 Project Structure

🛠️ Installation & Setup

Prerequisites

PowerShell

• PowerShell 5.1+ or PowerShell Core 7.0+
• No additional dependencies required

Python

• Python 3.8+
• uv package manager (recommended) or pip
• aiohttp library for async HTTP operations

Installation Steps

1. Install uv (if not already installed)

   root@kitploit:~

   # On macOS and Linux
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # On Windows
   powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
   
   # Alternative: use pip
   pip install uv
   

2. Clone the repository

   root@kitploit:~

   git clone <repository-url>
   cd CVE-2025-53770
   

3. Install Python dependencies

   root@kitploit:~

   # Install all dependencies (recommended)
   uv sync
   
   # Or install specific groups
   uv sync --group testing
   uv sync --group dev
   
   # Alternative with pip (if uv not available)
   pip install -e .
   

4. Verify installation

   root@kitploit:~

   # Test Python detector
   uv run python src/detectors/detector.py --help
   uv run cve-2025-53770-detect --help
   
   # Test PowerShell detector
   Get-Help .\src\detectors\detector.ps1
   

📖 Usage Guide

PowerShell Detector (detector.ps1)

Parameters

• TargetRange (Required): IP range to scan
  • Single IP: "192.168.1.100"
  • IP range: "192.168.1.1-50"
  • CIDR notation: "192.168.1.0/24" (basic support)
• OutputFile (Optional): Path to save JSON results
• Verbose (Optional): Enable detailed logging

Examples

Python Detector (src/detectors/detector.py)

Arguments

• --target-range, -t (Required): IP range to scan
• --output, -o (Optional): Output file for JSON results
• --verbose, -v (Optional): Enable verbose logging

Examples

🔧 Detection Methodology

Scanning Process

1. Network Discovery: Port scanning on common HTTP/HTTPS ports (80, 443, 8080, 8443, etc.)
2. HTTP Probing: Analyze HTTP responses, headers, and content
3. Pattern Detection: Search for deserialization frameworks and unsafe practices
4. Risk Assessment: Score vulnerabilities based on multiple indicators
5. Report Generation: Structured JSON output with detailed findings

Detection Patterns

The detectors identify:

• Java serialization frameworks (ObjectInputStream, BinaryFormatter)
• Unsafe deserialization patterns (pickle.loads, yaml.load)
• Application servers with known deserialization issues
• Missing input validation indicators
• Suspicious HTTP headers and content types

Risk Scoring

• Score 0-49: Low risk (informational findings)
• Score 50-69: Medium risk (potential vulnerability)
• Score 70+: High risk (likely vulnerable)

📊 Output Format

Both detectors generate structured JSON reports:

🏢 Enterprise Deployment

Network Scanning Considerations

• Firewall Configuration: Ensure scanning hosts can reach target networks
• Rate Limiting: Built-in timeouts prevent network flooding
• Authentication: No credentials required for detection scanning
• Logging: Comprehensive logging for audit trails

Integration Options

• SIEM Integration: JSON output compatible with major SIEM platforms
• CI/CD Pipelines: Automated vulnerability scanning in deployment workflows
• Scheduled Scanning: Use with cron/Task Scheduler for regular assessments
• Reporting Dashboards: Parse JSON results for executive reporting

Security Considerations

• Read-Only Operation: Detectors only perform reconnaissance, no exploitation
• Network Impact: Minimal network traffic, non-intrusive scanning
• Data Privacy: No sensitive data collection or storage
• False Positives: Risk scoring helps prioritize genuine vulnerabilities

🛡️ Remediation Guidance

Immediate Actions

1. Inventory Systems: Use detection results to identify vulnerable systems
2. Network Segmentation: Isolate vulnerable systems if possible
3. Patch Management: Apply vendor security updates for identified systems
4. Monitoring: Implement enhanced logging for deserialization activities

Long-Term Security Measures

1. Input Validation: Implement strict validation for all user inputs
2. Secure Deserialization: Use safe deserialization libraries and practices
3. Network Security: Deploy WAF rules to block malicious serialized payloads
4. Security Training: Educate developers on secure coding practices

📚 Documentation

• DETECTION_ALGORITHMS.md: Detailed pseudocode and algorithm documentation
• LIBRARIES.md: External dependencies and library documentation
• TASKS.md: Project development tasks and milestones
• TODO.md: Prioritized development backlog
• NOTES.md: Reference links and resources

🔗 References

• Microsoft Security Response Center - CVE-2025-53770
• CVE.org Record - CVE-2025-53770
• CWE-502: Deserialization of Untrusted Data

📋 System Requirements

PowerShell Environment

• OS: Windows 10+, Windows Server 2016+, or any OS with PowerShell Core
• PowerShell: Version 5.1+ or PowerShell Core 7.0+
• Network: Outbound connectivity to target ranges
• Permissions: Standard user permissions (no admin required)

Python Environment

• OS: Windows, Linux, macOS
• Python: Version 3.8+
• Memory: Minimum 512MB RAM for large network scans
• Network: Outbound connectivity to target ranges
• Package Manager: uv (recommended) or pip
• Dependencies: aiohttp (managed via pyproject.toml)

🚨 Disclaimer

This tool is designed for defensive security purposes only. It should only be used:

• On networks you own or have explicit permission to scan
• For vulnerability assessment and security testing
• By security professionals and system administrators
• In compliance with applicable laws and regulations

The authors are not responsible for any misuse of this tool.

📄 License

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

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/improvement)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/improvement)
5. Create a Pull Request

📞 Support

For issues, questions, or contributions:

• Create an issue in the project repository
• Review existing documentation in the /docs folder
• Check the troubleshooting section in project documentation

Last Updated: July 28, 2025
Version: 1.0.0
Maintainer: Security Research Team