CVE-2025-53770 反序列化漏洞

漏洞信息

漏洞名称： CVE-2025-53770 反序列化漏洞

漏洞编号：

CVE： CVE-2025-53770

漏洞类型： 反序列化

漏洞等级： 高危

漏洞描述： CVE-2025-53770是一个与不安全反序列化相关的安全漏洞，属于CWE-502（反序列化不可信数据）类别。该漏洞允许攻击者通过构造恶意的序列化数据，在目标系统上执行远程代码，从而可能导致严重的安全问题。

受影响产品: 该漏洞主要影响使用Java序列化框架（如ObjectInputStream、BinaryFormatter）或其他不安全反序列化实践（如pickle.loads、yaml.load）的应用服务器和应用程序。这些产品通常部署在企业环境中，用于提供各种网络服务。

漏洞解释: 漏洞的根本原因在于应用程序在反序列化过程中未对输入数据进行充分的验证和过滤，导致攻击者可以注入恶意的序列化对象。当这些对象被反序列化时，攻击者可以在目标系统上执行任意代码。这种类型的漏洞通常由于开发者在实现序列化和反序列化功能时，未能遵循安全最佳实践所致。

影响分析: 该漏洞的安全风险极高，攻击者可以利用它进行远程代码执行，从而完全控制受影响的系统。此外，由于反序列化操作通常不需要用户认证，攻击者可以无需任何权限即可利用此漏洞。这意味着漏洞可以被自动化工具大规模利用，对企业和组织的网络安全构成严重威胁。为了防止此类攻击，建议立即应用相关的安全补丁，并对所有输入数据进行严格的验证和过滤。

来源： https://github.com/daryllundy/CVE-2025-53770

类型： CVE-2025:github search

仓库文件

.claude
.gitignore
CLAUDE.md
DETECTION_ALGORITHMS.md
LIBRARIES.md
NOTES.md
PROJ_NOTES.md
README.md
TASKS.md
TODO.md
detectors
pyproject.toml
testing
uv.lock

来源概述

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
.\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

## 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

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

Clone the repository

1 2	`git clone <repository-url> cd CVE-2025-53770`

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 .

Verify installation

# 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

## 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

## 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

Network Discovery: Port scanning on common HTTP/HTTPS ports (80, 443, 8080, 8443, etc.)
HTTP Probing: Analyze HTTP responses, headers, and content
Pattern Detection: Search for deserialization frameworks and unsafe practices
Risk Assessment: Score vulnerabilities based on multiple indicators
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

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

Long-Term Security Measures

Input Validation: Implement strict validation for all user inputs
Secure Deserialization: Use safe deserialization libraries and practices
Network Security: Deploy WAF rules to block malicious serialized payloads
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

📋 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

Fork the repository
Create a feature branch (git checkout -b feature/improvement)
Commit your changes (git commit -am 'Add new feature')
Push to the branch (git push origin feature/improvement)
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

Github Poc

#反序列化 #CVE-2025:github search

CVE-2025-53770 反序列化漏洞

http://example.com/2025/07/28/github_4137756665/

作者

lianccc

发布于

2025年7月28日

许可协议

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Periodic Script Persistence 下一篇

CVE-2025-53770 反序列化漏洞

漏洞信息

仓库文件

来源概述

CVE-2025-53770 Detection Project

🔍 Overview

Vulnerability Details

🚀 Quick Start

PowerShell Detector

Python Detector

📁 Project Structure

🛠️ Installation & Setup

Prerequisites

PowerShell

Python

Installation Steps

📖 Usage Guide

PowerShell Detector (detector.ps1)

Parameters

Examples

Python Detector (detector.py)

Arguments

Examples

🔧 Detection Methodology

Scanning Process

Detection Patterns

Risk Scoring

📊 Output Format

🏢 Enterprise Deployment

Network Scanning Considerations

Integration Options

Security Considerations

🛡️ Remediation Guidance

Immediate Actions

Long-Term Security Measures

📚 Documentation

🔗 References

📋 System Requirements

PowerShell Environment

Python Environment

🚨 Disclaimer

📄 License

🤝 Contributing

📞 Support

PowerShell Detector (`detector.ps1`)

Python Detector (`detector.py`)