Sekiryu is a comprehensive toolkit for automating Ghidra's headless analysis, targeting reverse engineers and security researchers. It streamlines vulnerability hunting, code commenting via ChatGPT, and report generation with data visualization, offering significant time savings for complex binary analysis tasks.

How It Works

Sekiryu leverages Ghidra's headless mode for batch processing and provides a script repository for diverse analysis functions. It supports custom script integration via an XML-RPC server, allowing scripts to interact with Ghidra and the host system. This architecture enables flexible input (single files or folders) and extensibility for user-defined analysis workflows.

Quick Start & Requirements

• Install via pip: pip install sekiryu
• Prerequisites: Ghidra (download from GitHub), Java JDK 17+, optional: BinExport, Semgrep.
• Usage: sekiryu [-F FILE][OPTIONS]
• Official Ghidra download: https://github.com/NationalSecurityAgency/ghidra
• Semgrep rules: https://github.com/0xdea/semgrep-rules

Highlighted Details

• Vulnerability hunting using pattern recognition and Semgrep rules.
• Automatic pseudo-code generation and commenting with ChatGPT.
• Reporting with data visualization capabilities.
• Extensible API for custom script development and interaction.

Maintenance & Community

The project is a personal initiative, with the author seeking contributions for optimization, malware analysis scripts, and data visualization improvements. Further details can be found on their website: https://www.bushido-sec.com/.

Licensing & Compatibility

The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project is described as the author's first "big" project, and a more advanced alternative is recommended (https://github.com/LaurieWired/GhidraMCP). The XML-RPC server is noted as potentially insecure against maliciously constructed data. Binary analysis can be a slow process, requiring significant waiting time for large inputs.