## https://sploitus.com/exploit?id=5AE88448-C9F5-5FE6-9EF3-46D80BDD6CC9
# CVE-2025-53770 Detection Project
A comprehensive vulnerability detection toolkit for identifying systems vulnerable to **CVE-2025-53770** (CWE-502: Deserialization of Untrusted Data).
## 🔍 Overview
This project provides enterprise-ready detection scripts to identify and assess systems potentially vulnerable to CVE-2025-53770, a security vulnerability related to unsafe deserialization of untrusted data. The toolkit includes both PowerShell and Python implementations for maximum compatibility across different environments.
### Vulnerability Details
- **CVE ID**: CVE-2025-53770
- **CWE Classification**: CWE-502 (Deserialization of Untrusted Data)
- **Severity**: Variable (depends on implementation and exposure)
- **Description**: Vulnerability in deserialization processes that can lead to remote code execution
## 🚀 Quick Start
### PowerShell Detector
```powershell
# Basic scan
.\detectors\detector.ps1 -TargetRange "192.168.1.1-50"
# Scan with output file and verbose logging
.\detectors\detector.ps1 -TargetRange "192.168.1.0/24" -OutputFile "results.json" -Verbose
```
### Python Detector
```bash
# Install with uv (recommended)
uv sync
# Basic scan
uv run python detectors/detector.py --target-range "192.168.1.1-50"
# Scan with output file and verbose logging
uv run python detectors/detector.py --target-range "10.0.0.0/24" --output results.json --verbose
# Or use the installed script
uv run cve-2025-53770-detect --target-range "192.168.1.1-50"
```
## 📁 Project Structure
```
CVE-2025-53770/
├── README.md # Project documentation
├── CLAUDE.md # Claude Code instructions
├── NOTES.md # Reference links and resources
├── TASKS.md # Project task tracking
├── TODO.md # Prioritized backlog
├── DETECTION_ALGORITHMS.md # Pseudocode and algorithm documentation
├── LIBRARIES.md # External dependencies documentation
└── detectors/ # Detection scripts
├── detector.ps1 # PowerShell implementation
├── detector.py # Python implementation
└── requirements.txt # Python dependencies
```
## 🛠️ 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)**
```bash
# 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**
```bash
git clone
cd CVE-2025-53770
```
3. **Install Python dependencies**
```bash
# 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**
```bash
# Test Python detector
uv run python detectors/detector.py --help
uv run cve-2025-53770-detect --help
# Test PowerShell detector
Get-Help .\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
```powershell
# Scan single subnet with verbose output
.\detector.ps1 -TargetRange "192.168.1.1-254" -Verbose
# Enterprise scan with results export
.\detector.ps1 -TargetRange "10.0.0.1-100" -OutputFile "enterprise_scan.json"
```
### Python Detector (`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
```bash
# Comprehensive network scan
uv run python detectors/detector.py --target-range "192.168.0.0/24" --verbose
# Targeted scan with results export
uv run python detectors/detector.py -t "10.0.1.1-50" -o vulnerability_report.json
# Using the installed command
uv run cve-2025-53770-detect --target-range "192.168.0.0/24" --output scan_results.json
```
## 🔧 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:
```json
{
"scan_metadata": {
"cve_id": "CVE-2025-53770",
"scan_date": "2025-07-28T10:30:00Z",
"scanner_version": "Python-1.0",
"target_range": "192.168.1.1-50",
"total_vulnerabilities": 3,
"total_high_severity": 1,
"total_medium_severity": 2,
"total_low_severity": 0
},
"vulnerabilities": [
{
"timestamp": "2025-07-28T10:30:15Z",
"target": {
"ip_address": "192.168.1.100",
"port": 8080
},
"vulnerability": {
"cve_id": "CVE-2025-53770",
"cwe_id": "CWE-502",
"description": "Deserialization of Untrusted Data",
"severity": "HIGH",
"score": 75,
"is_vulnerable": true
},
"findings": [
"Java application server detected: Apache Tomcat",
"Deserialization pattern detected: ObjectInputStream"
],
"technical_details": {
"probe_results": { /* HTTP response data */ },
"scan_method": "Python Async HTTP Probe"
}
}
]
}
```
## 🏢 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](DETECTION_ALGORITHMS.md)**: Detailed pseudocode and algorithm documentation
- **[LIBRARIES.md](LIBRARIES.md)**: External dependencies and library documentation
- **[TASKS.md](TASKS.md)**: Project development tasks and milestones
- **[TODO.md](TODO.md)**: Prioritized development backlog
- **[NOTES.md](NOTES.md)**: Reference links and resources
## 🔗 References
- [Microsoft Security Response Center - CVE-2025-53770](https://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-53770)
- [CVE.org Record - CVE-2025-53770](https://vulners.com/cve/CVE-2025-53770)
- [CWE-502: Deserialization of Untrusted Data](https://cwe.mitre.org/data/definitions/502.html)
## 📋 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