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介绍

# 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 <repository-url>
   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://www.cve.org/CVERecord?id=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

文件快照

 [4.0K]  /data/pocs/0b8c2fd733dfcbc4cc6ae6c6a8ba88d922598844
├── [1.3K]  CLAUDE.md
├── [4.1K]  DETECTION_ALGORITHMS.md
├── [4.0K]  detectors
│   ├── [9.7K]  detector.ps1
│   ├── [ 15K]  detector.py
│   └── [ 406]  __init__.py
├── [3.4K]  LIBRARIES.md
├── [ 165]  NOTES.md
├── [3.3K]  PROJ_NOTES.md
├── [3.5K]  pyproject.toml
├── [ 10K]  README.md
├── [2.1K]  TASKS.md
├── [4.0K]  testing
│   ├── [4.0K]  docs
│   │   ├── [ 13K]  TESTING_GUIDE.md
│   │   └── [ 16K]  VALIDATION_PROCEDURES.md
│   ├── [4.0K]  mock-server
│   │   ├── [ 14K]  mock_server.py
│   │   └── [1.8K]  README.md
│   ├── [7.4K]  README.md
│   ├── [4.0K]  test-data
│   │   ├── [ 12K]  sample_test_data.json
│   │   └── [ 19K]  test_data_generator.py
│   └── [4.0K]  unit-tests
│       ├── [ 360]  pytest.ini
│       ├── [3.8K]  run_tests.py
│       ├── [ 13K]  test_powershell_detector.py
│       └── [ 17K]  test_python_detector.py
├── [ 866]  TODO.md
└── [440K]  uv.lock

6 directories, 24 files

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