Gpu-Abuser-Malware

Overview

Welcome to the Gpu-Abuser-Malware repository! This project explores how GPUs can be misused for executing potentially malicious activities such as payload decryption, leveraging the computational power of GPUs to evade traditional CPU-based detection mechanisms.

The goal of this repository is to document my research and provide practical examples of GPU-based malware techniques for educational purposes. This repository will be updated as I explore and document new strategies.

Table of Contents

• Overview
• Techniques Included
• Installation
• Usage
• Contributing
• Disclaimer
• License

Techniques Included

This repository currently includes the following techniques:

• GPU-Based Decryption: Using the GPU to perform decryption operations for potentially malicious payloads.

  • PayloadDecryption: A sample demonstrating payload decryption using OpenCL.

• Anti-Detection: Techniques to bypass detection by offloading processing to the GPU.

  • AntiDebuggingUsingGPU: Methods to hinder debugging by utilizing the GPU for processing.

Installation

To set up and run the examples in this repository:

1. Clone the Repository:

   git clone https://github.com/YourUsername/Gpu-Abuser-Malware.git

2. Install Dependencies:

   • Make sure you have the OpenCL SDK and libraries installed on your system.
   • Install the required GPU drivers that support OpenCL.

3. Build the Solution:

   • Open the project in Visual Studio or your preferred IDE.
   • Build the solution using the appropriate configuration (Debug/Release).

4. Run the Executables:

   • Execute the compiled binaries to observe the GPU-based malware techniques in action.

Usage

Each directory contains a specific technique with relevant source code. You can explore the code, build it, and run it to see how the techniques work. The code is documented to help you understand the logic behind each technique.

# Example
cd PayloadDecryption
# Open the solution and build the project

Contributing

Contributions are welcome! If you have additional GPU-based techniques or improvements to existing ones, feel free to submit a pull request. Please ensure your contributions adhere to ethical guidelines and are intended for educational and research purposes only.

Disclaimer

This repository is for educational and research purposes only. The techniques demonstrated here are intended to help security professionals understand and defend against GPU-based malware evasion tactics. Misuse of the information contained in this repository is not condoned by the author.

License

This project is licensed under the MIT License. See the LICENSE file for details.