yarobot 0.4.2

YARA generator inspired by yarGen

yarobot

yarobot is a high-performance YARA rule generator inspired by yarGen project, designed to automatically create quality YARA rules from malware samples while minimizing false positives through intelligent goodware database comparison.

✨ Features

• Automated YARA Rule Generation: Create both simple and super rules from malware samples
• Advanced Scoring System: String scoring with goodware database comparison
• High-Performance Engine: Rust-based core stringZZ for fast file processing
• Multiple Interfaces: CLI, Python API, and web interface
• Intelligent Filtering: Automatic exclusion of common goodware strings for your specific dataset
• Super Rules: Automatic creation of rules that match multiple related samples

🏗️ Architecture

flowchart TD
    A[CLI] --> D
    B[Web Upload] --> D
    C[API Call] --> D
    
    D[Token extraction] --> E[Scoring]
    F[Goodware DB] --> E
    
    E --> G[YARA Generator]
    G --> H[Rule file]
    G --> I[Web Display]
    G --> J[API JSON]

🛠 Installation

1. Install from PyPI

pip install yarobot

2. Install from Source

# Clone repository
git clone https://github.com/ogre2007/yarobot
cd yarobot

# Install in development mode
pip install -e .

# Or install with all dependencies
pip install ".[dev]"

📖 Quick Start

1. First-Time Setup (optional but recommended)

# Create a goodware database
mkdir -p ./dbs
py -m yarobot.database create /path/to/goodware/files --recursive --opcodes

# The database will be saved in ./dbs/

2. Generate Your First Rules

# Basic rule generation
py -m yarobot.generate /path/to/malware/samples \
  --output-rule-file my_rules.yar \
  --author "Your Name" \
  --ref "Case-001"

3. Launch Web Interface

# Start with your database
py -m yarobot.app -g ./dbs

# Access at http://localhost:5000

then locate http://localhost:5000 or use api directly from anywhere:

curl -X POST -F "files=@tests\\data\\binary" http://localhost:5000/api/analyze -F "min_score=5" -F "get_opcodes=true"

4. Advanced Configuration

py -m yarobot.generate /malware/samples -g <goodware dbs path> \
  --opcodes \
  --recursive \
  --author "My Security Team" \
  --ref "Internal Investigation 2024" \
  --superrule-overlap 5 \
  --strings-per-rule 15

5. Database Management

# Update existing database with new goodware samples
(TODO) py -m yarobot.database update /path/to/new/goodware --identifier corporate 

# Create new database from scratch
py -m yarobot.database create /path/to/goodware --opcodes

🔧 Configuration Options

Rule Generation Options

• --min-size, --max-size: String length boundaries
• --min-score: Minimum string score threshold
• --opcodes: Enable opcode feature for additional detection capabilities
• --superrule-overlap: Minimum overlapping strings for super rule creation
• --recursive: Scan directories recursively
• --excludegood: Force exclusion of all goodware strings
• --oe: only executable extensions

Database Options

• --identifier: Database identifier for multi-environment support
• --update: Update existing databases with new samples
• --only-executable: Only process executable file extensions

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

TODO's

• Global project refactoring & packaging
• Token extraction rewritten in Rust
• Tests & CI/CD pipeline
• Multiplatform PyPI release
• HTTP service with web UI
• Store regex patterns in configuration
• Wide/ASCII token merging
• Token deduplication
• Fix/improve imphash/exports handling
• Include default databases
• Rule generation improvements
• Separate token extraction to stringZZ package
• Regexp generation
• LLM Scoring support

📄 License

This project is licensed under the GPLv3 License - see the LICENSE file for details.

🙏 Credits

• yarGen by Florian Roth (initial idea and implementation)
• Pyo3 for Python-Rust integration
• goblin for binary parsing

📞 Support

• Issues: GitHub Issues