AI-Powered Source Code Security Analyzer
Static analysis • AI-driven insights • Local-first privacy
Features • Quick Start • Screenshots • Documentation • Contributing
ShieldEye NeuralScan is a comprehensive security code analyzer that combines traditional static analysis with cutting-edge AI technology. It provides:
- 🔍 Advanced static analysis with 50+ security patterns for common vulnerabilities
- 🤖 AI-powered code review using local transformer models (StarCoder2, Mixtral)
- 🐳 Container security scanning via optional Trivy integration
- 📊 Real-time threat scoring with risk categorization and compliance mapping
- 🖥️ Modern GTK4 desktop interface with intuitive navigation and dark theme
- 🔒 100% local-first architecture – all analysis happens on your machine
Whether you're a security researcher, developer, or DevSecOps engineer, ShieldEye NeuralScan delivers actionable insights into your code's security posture.
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| Dashboard | Results |
|---|---|
|
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| Security posture overview and threat activity | Detailed findings with severity levels |
| Scan Configuration | Settings |
|---|---|
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| File selection and scan detail level options | AI model and scanner configuration |
ShieldEye NeuralScan uses a modular desktop architecture for performance and maintainability:
┌──────────────────────────────────────────────────────────────┐
│ GTK4 Desktop Interface │
│ (Python 3 + PyGObject) │
│ ┌──────────┬──────────┬──────────┬──────────┬──────────┐ │
│ │Dashboard │ Scan │ Results │ Settings │ About │ │
│ └──────────┴──────────┴──────────┴──────────┴──────────┘ │
└─────────────────────────────┬────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────┐
│ Security Scanner Engine │
│ (Static Analysis + AI Integration) │
│ ┌──────────────┬──────────────┬──────────────────────┐ │
│ │ Heuristic │ AI Analyzer │ Trivy Integration │ │
│ │ Patterns │ (Transformers)│ (Docker/Optional) │ │
│ └──────────────┴──────────────┴──────────────────────┘ │
└─────────────────────────────┬────────────────────────────────┘
│
▼
┌───────────────────────┐
│ Local File System │
│ data/scan_history │
│ data/config.json │
└───────────────────────┘
| Component | Technology | Purpose |
|---|---|---|
| GUI | GTK 4.0, PyGObject | Native desktop interface |
| AI Engine | Hugging Face Transformers | Code analysis and explanations |
| Static Analysis | Python regex, AST parsing | Pattern-based vulnerability detection |
| Container Scanning | Trivy (optional) | Dependency and image vulnerability scanning |
| Data Visualization | Matplotlib | Threat activity charts |
| Storage | JSON files | Scan history and configuration |
| Requirement | Version | Notes |
|---|---|---|
| Python | 3.10+ | With pip and venv |
| GTK | 4.0+ | Desktop environment required |
| Git | Latest | For cloning repository |
| Docker | Latest | Optional, for Trivy integration |
git clone https://github.com/exiv703/ShieldEye-NeuralScan.git
cd ShieldEye-NeuralScan
# Create environment file (optional)
cp .env.example .env
# Edit .env to customize AI model, window size, etc.# Make run script executable
chmod +x run.sh
# Install all dependencies (creates venv, installs packages)
./run.sh --mode install# Interactive launcher with menu
./run.sh
# Or run directly
./run.sh runAI models download automatically on first scan. For GPU acceleration:
# Check CUDA availability
python -c "import torch; print(torch.cuda.is_available())"
# If True, AI will use GPU automatically
# If False, CPU inference will be used (slower but functional)# Install Docker
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
# Start Docker service
sudo systemctl start docker
sudo systemctl enable docker
# Enable Trivy in Settings or .env
USE_TRIVY=trueThe run.sh script provides an interactive menu for common tasks:
./run.shAvailable options:
- Run – Launch the application
- Install – Set up virtual environment and dependencies
- Update – Pull latest changes and update dependencies
- Clean – Remove virtual environment and cached files
- Test – Run test suite
- Help – Display usage information
Copy .env.example to .env and customize:
# Application Settings
APP_NAME=ShieldEye NeuralScan
APP_VERSION=1.0.0
APP_ENV=development
# AI Model Configuration
AI_MODEL=bigcode/starcoder2-3b
# Alternatives: bigcode/starcoder2-7b, mistralai/Mixtral-8x7B-Instruct-v0.1
# Scanner Settings
USE_TRIVY=false
SAVE_HISTORY=true
DEFAULT_DETAIL_LEVEL=standard
# UI Settings
WINDOW_WIDTH=1400
WINDOW_HEIGHT=900
THEME=darkSee requirements.txt for Python dependencies and requirements-dev.txt for development tools.
- User Guide: Comprehensive usage instructions and best practices
- API Reference: Docstrings in
backend/scanner.pyfor programmatic usage - Security Patterns: Full list of detection rules in scanner source code
- Test Files: Example vulnerable code in
tests/directory
# Clone repository
git clone https://github.com/exiv703/ShieldEye-NeuralScan.git
cd ShieldEye-NeuralScan
# Create virtual environment
python3 -m venv venv
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt
# Run application
python gui/main.py# Format code
black backend/ gui/ utils/
isort backend/ gui/ utils/
# Lint code
pylint backend/scanner.py gui/ utils/
# Run tests
python -m pytest tests/ShieldEye-NeuralScan/
├── backend/
│ ├── __init__.py
│ └── scanner.py # Core security scanner engine
├── gui/
│ ├── views/
│ │ ├── dashboard.py # Overview and metrics
│ │ ├── scan.py # File selection and scanning
│ │ ├── results.py # Findings display
│ │ └── settings.py # Configuration panel
│ ├── main.py # Application entry point
│ ├── window.py # Main window and navigation
│ └── style.css # GTK CSS theming
├── utils/
│ └── file_handler.py # Scan history persistence
├── tests/ # Vulnerable test files
├── data/ # Scan history and config
├── assets/ # Screenshots and branding
├── .env.example # Environment template
├── config.default.json # Default configuration
├── requirements.txt # Python dependencies
└── run.sh # Interactive launcher
Design Principles:
- Clean separation of GUI and business logic
- Modular view system with independent components
- Comprehensive error handling and logging
- Production-ready configuration management
- Privacy-first architecture with local-only processing
Contributions are welcome! Here's how to get started:
- Fork the repository
- Create a feature branch:
git checkout -b feature/amazing-feature - Commit your changes:
git commit -m 'Add amazing feature' - Push to the branch:
git push origin feature/amazing-feature - Open a Pull Request
Guidelines:
- Follow PEP 8 style guidelines
- Add tests for new security patterns
- Update documentation for new features
- Ensure all tests pass before submitting
This project is licensed under the MIT License – see the LICENSE file for details.
- Hugging Face – Transformers library and model hosting
- GTK Project – Cross-platform GUI toolkit
- Aqua Security – Trivy container scanner
- BigCode – StarCoder2 models
- Mistral AI – Mixtral models
⭐ If you find ShieldEye NeuralScan useful, please consider giving it a star! ⭐
Built with ❤️ for the security community
ShieldEye NeuralScan – Securing code with AI, one scan at a time 🛡️