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

# Basic scan
.\src\detectors\detector.ps1 -TargetRange "192.168.1.1-50"

# Scan with output file and verbose logging
.\src\detectors\detector.ps1 -TargetRange "192.168.1.0/24" -OutputFile "results.json" -Verbose

Python Detector

# Install with uv (recommended)
uv sync

# Basic scan
uv run python src/detectors/detector.py --target-range "192.168.1.1-50"

# Scan with output file and verbose logging
uv run python src/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
├── docs/
│   ├── CLAUDE.md
│   ├── DETECTION_ALGORITHMS.md
│   ├── LIBRARIES.md
│   ├── NOTES.md
│   ├── PROJ_NOTES.md
│   ├── TASKS.md
│   └── TODO.md
├── src/
│   └── detectors/
│       ├── __init__.py
│       ├── detector.py
│       └── detector.ps1
├── tests/
│   ├── README.md
│   ├── mock_server/
│   ├── test_data/
│   └── unit/
├── .gitignore
├── pyproject.toml
└── uv.lock

🛠️ 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)

   # 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

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

3. Install Python dependencies

   # 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

   # 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

# Scan single subnet with verbose output
.\src\detectors\detector.ps1 -TargetRange "192.168.1.1-254" -Verbose

# Enterprise scan with results export
.\src\detectors\detector.ps1 -TargetRange "10.0.0.1-100" -OutputFile "enterprise_scan.json"

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

# Comprehensive network scan
uv run python src/detectors/detector.py --target-range "192.168.0.0/24" --verbose

# Targeted scan with results export
uv run python src/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:

{
  "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