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LLM Decompiler

A powerful decompilation tool that leverages Large Language Models (LLMs) to reverse engineer and decompile binary code into human-readable source code.

Features

  • Advanced Decompilation: Converts compiled binaries back into high-level source code
  • LLM-Powered Analysis: Utilizes state-of-the-art language models for improved code reconstruction
  • Multiple Architecture Support: Works with various CPU architectures
  • Interactive Mode: Step through the decompilation process interactively
  • Plugin System: Extensible architecture for adding new decompilation strategies

Prerequisites

  • Python 3.8+
  • pip (Python package manager)
  • (Optional) CUDA for GPU acceleration

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/llm-decompiler.git
    cd llm-decompiler
  2. Create and activate a virtual environment (recommended):

    python -m venv venv
    .\venv\Scripts\activate  # On Windows
    # or
    source venv/bin/activate  # On Unix or MacOS
  3. Install the required dependencies:

    pip install -r requirements.txt

Usage

Basic Usage

python decompile.py <binary_file> [options]

Options

  • -o, --output: Specify output file (default: stdout)
  • -a, --arch: Target architecture (x86, x64, arm, etc.)
  • -v, --verbose: Enable verbose output
  • --interactive: Launch interactive decompilation session

Example

python decompile.py example.exe -o decompiled.c

Project Structure

.
├── src/                 # Source code
│   ├── core/           # Core decompilation logic
│   ├── llm/            # LLM integration
│   └── utils/          # Utility functions
├── tests/              # Test suite
├── examples/           # Example binaries and outputs
├── docs/               # Documentation
└── README.md           # This file

Contributing

Contributions are welcome! Please read our Contributing Guidelines for details on our code of conduct and the process for submitting pull requests.

License

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

Acknowledgments

  • Thanks to all the open-source projects that made this possible
  • Special thanks to our contributors

Support

For support, please open an issue on our GitHub Issues page.

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