Phantom

This repository contains a PHP vulnerability scanner, a tool designed by Chat-GPT to identify security vulnerabilities in PHP applications. The scanner uses utilized various regexto detect common security weaknesses and potential exploits in PHP code.

Features

• Static Analysis: The scanner performs static analysis on PHP source code to identify potential vulnerabilities. It analyzes the code structure, variable usage, function calls, and other patterns to detect security flaws.

• Vulnerability Detection: The scanner incorporates a set of predefined rules and checks to identify common vulnerabilities such as SQL injection, cross-site scripting (XSS), remote code execution, file inclusion, and more.

  • Detected Vulnerabilities
    • XSS
    • SQL Injection
    • OS Command Injection
    • LFI
    • Unristricted File Upload (Potential)
    • SSRF (Under Development)

• WEB-UI: open the tool web page throgh http://127.0.0.1:5000 and start Upload your Php File.

• Extensibility: The tool allows for easy extensibility by providing an API that allows developers to create custom vulnerability checks and add them to the scanning process.

• Dashbiard Reporting: After scanning a PHP application, the scanner generates a detailed report highlighting the identified vulnerabilities, including their severity, affected code snippets, and recommendations for remediation.

Requirements

• Python >= 3.0
• flask

Installation

1. Clone the repository:

     git clone https://github.com/0xBugatti/Phantom.git

2. Change to the project directory:

    cd Phantom/ 

3. Install the dependencies using Composer:

   pip install falsk

Usage

1. Navigate to the project directory.

2. Run the vulnerability scanner command, providing the path to the PHP application you want to scan:

   python Phantom.py

3. Open Url http://127.0.0.1:5000.

4. Upload you php file.

5. Once the scanning process is complete, the tool will generate a report file containing information about the identified vulnerabilities.

Contributing

Contributions to this project are welcome! If you encounter any issues or have ideas for improvements, please open an issue or submit a pull request on the GitHub repository.

When contributing, please ensure you follow the existing coding style and include appropriate tests for any new features or bug fixes.

License

This project is licensed under the MIT License. Feel free to use, modify, and distribute this code for both commercial and non-commercial purposes.