Bootcamp week 4: Add SecureCode AI - an AI-powered code security and performance analyzer
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Mohamed Salah
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week4/community-contributions/salah/securecode-ai/README.md
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# SecureCode AI
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**AI-Powered Code Security & Performance Analyzer**
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Built for Week 4 of the LLM Engineering course - A novel solution that addresses real-world needs not covered by other community contributions.
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## Why SecureCode AI?
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Unlike other Week 4 projects that focus on docstrings or code conversion, **SecureCode AI** provides:
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✅ **Security vulnerability detection** (OWASP Top 10)
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✅ **Performance bottleneck analysis** (Big-O, complexity)
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✅ **Automated fix generation** with explanations
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✅ **Unit test generation** (happy path + edge cases)
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✅ **Educational focus** - teaches WHY code is vulnerable/slow
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Perfect for developers learning secure coding practices and performance optimization!
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## Features
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### 🔒 Security Analysis
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Detects real vulnerabilities following OWASP guidelines:
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- SQL Injection, XSS, Command Injection
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- Path Traversal, Insecure Deserialization
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- Hardcoded Credentials, Cryptographic Failures
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- Authentication/Authorization Issues
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### ⚡ Performance Analysis
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Identifies performance issues:
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- Time/Space Complexity (Big-O analysis)
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- Inefficient Algorithms (nested loops, N+1 queries)
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- Memory Leaks, Caching Opportunities
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- Blocking I/O Operations
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### 🔧 Auto-Fix Generation
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Automatically generates:
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- Secure code alternatives
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- Optimized implementations
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- Line-by-line explanations
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- Best practice recommendations
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### 🧪 Unit Test Generation
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Creates comprehensive test suites:
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- pytest/unittest compatible
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- Happy path, edge cases, error handling
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- Parameterized tests
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- Test fixtures and mocks
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### 🌍 Multi-Language Support
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Python, JavaScript, Java, C++, Go, Rust with auto-detection
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### 🤖 Model Agnostic
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Works with any OpenRouter model - free tier available!
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## Quick Start
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See [QUICKSTART.md](QUICKSTART.md) for detailed setup instructions.
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### TL;DR - 2 Steps to Run (using uvx)
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```bash
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# 1. Configure (get free API key from openrouter.ai)
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cd week4/securecode-ai
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cp .env.example .env
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# Edit .env and add: OPENROUTER_API_KEY=your-key-here
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# 2. Run (uvx handles everything else!)
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./run.sh
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# Or run manually:
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# uvx --with gradio --with openai --with python-dotenv python main.py
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```
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**That's it!** No installation needed - `uvx` handles all dependencies automatically.
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The Gradio interface opens automatically at `http://localhost:7860`
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**First Time?** The default model is **FREE** - no credit card needed!
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## Usage
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### Security Analysis
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1. Go to the "🔒 Security Analysis" tab
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2. Paste your code
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3. Select language (or use Auto-detect)
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4. Click "🔍 Analyze Security"
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5. Review the identified vulnerabilities
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### Performance Analysis
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1. Go to the "⚡ Performance Analysis" tab
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2. Paste your code
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3. Select language (or use Auto-detect)
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4. Click "🚀 Analyze Performance"
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5. Review performance issues and optimization suggestions
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### Generate Fix
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1. Go to the "🔧 Generate Fix" tab
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2. Paste your original code
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3. Paste the analysis report (from Security or Performance tab)
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4. Select language (or use Auto-detect)
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5. Click "✨ Generate Fix"
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6. Review the fixed code and explanations
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### Generate Tests
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1. Go to the "🧪 Generate Tests" tab
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2. Paste your code (functions or classes)
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3. Select language (or use Auto-detect)
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4. Click "🧪 Generate Tests"
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5. Get complete pytest test file with:
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- Happy path tests
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- Edge cases
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- Error handling tests
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- Test fixtures if needed
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## Example Code
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Try the example code in `examples/`:
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- `vulnerable_code.py` - Code with security issues
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- `slow_code.py` - Code with performance issues
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- `sample_functions.py` - Clean functions for test generation
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## Configuration
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### Changing Models
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Edit `.env` to use different models:
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```bash
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# Free tier models (recommended for testing)
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SECURECODE_MODEL=meta-llama/llama-3.1-8b-instruct:free
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SECURECODE_MODEL=google/gemini-2.0-flash-exp:free
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# Paid models (better quality)
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SECURECODE_MODEL=openai/gpt-4o-mini
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SECURECODE_MODEL=anthropic/claude-3.5-sonnet
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SECURECODE_MODEL=qwen/qwen-2.5-coder-32b-instruct
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```
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Browse all available models at: https://openrouter.ai/models
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## Project Structure
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Clean, modular Python architecture following best practices:
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```
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securecode-ai/
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├── src/securecode/ # Main package
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│ ├── analyzers/ # Analysis engines
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│ │ ├── base_analyzer.py # Base class with OpenRouter client
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│ │ ├── security_analyzer.py # OWASP security analysis
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│ │ ├── performance_analyzer.py # Performance profiling
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│ │ ├── fix_generator.py # Auto-fix generation
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│ │ └── test_generator.py # Unit test creation
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│ ├── prompts/ # Specialized AI prompts
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│ │ ├── security_prompts.py # Security expert persona
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│ │ ├── performance_prompts.py # Performance engineer persona
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│ │ ├── fix_prompts.py # Code fixing prompts
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│ │ └── test_prompts.py # Test generation prompts
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│ ├── utils/
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│ │ └── language_detector.py # Auto-detect code language
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│ ├── config.py # Environment config
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│ └── app.py # Gradio UI (4 tabs)
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├── examples/ # Test code samples
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│ ├── vulnerable_code.py # SQL injection, etc.
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│ ├── slow_code.py # O(n²) algorithms
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│ └── sample_functions.py # Clean code for testing
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├── main.py # Application entry point
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├── pyproject.toml # Modern Python packaging
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├── .env.example # Configuration template
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├── setup.sh # Automated setup script
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├── QUICKSTART.md # Detailed setup guide
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└── README.md # This file
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```
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**Design Principles:**
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- **Separation of Concerns**: Each analyzer is independent
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- **DRY**: Base class handles OpenRouter communication
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- **Extensible**: Easy to add new analyzers
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- **Clean Code**: Type hints, docstrings, descriptive names
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## Development
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### Install development dependencies
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```bash
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pip install -e ".[dev]"
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```
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### Code formatting
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```bash
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black src/
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ruff check src/
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```
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### Running tests
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```bash
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pytest
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```
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## How It Works
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### Architecture
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```
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User Code → Language Detection → Specialized Prompt → OpenRouter API → AI Model
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↓
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User Interface ← Streaming Response ← Analysis/Fix/Tests ← Model Response
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```
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### Technical Implementation
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1. **Multi-Analyzer Pattern**: Separate classes for security, performance, fixes, and tests
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2. **Specialized Prompts**: Each analyzer uses persona-based prompts (security expert, performance engineer, etc.)
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3. **Streaming Responses**: Real-time output using Gradio's streaming capabilities
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4. **Model Agnostic**: Works with any OpenAI-compatible API through OpenRouter
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5. **Clean Code**: Type hints, docstrings, modular design
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### Example: Security Analysis Flow
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```python
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# User pastes code
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code = "query = f'SELECT * FROM users WHERE id = {user_id}'"
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# Security analyzer builds prompt
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prompt = SecurityPrompt(code, language="Python")
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# Calls AI model via OpenRouter
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response = openai.chat.completions.create(
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model="meta-llama/llama-3.1-8b-instruct:free",
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messages=[
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{"role": "system", "content": SECURITY_EXPERT_PROMPT},
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{"role": "user", "content": code}
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],
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stream=True
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)
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# Streams results to UI
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for chunk in response:
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yield chunk # Real-time display
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```
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## Cost Considerations
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- **Free Tier Models**: Use models with `:free` suffix (rate-limited but no cost)
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- **Paid Models**: More accurate but incur API costs (~$0.001-0.01 per analysis)
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- **Recommended**: Start with `meta-llama/llama-3.1-8b-instruct:free` for testing
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## Limitations
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- Analysis quality depends on the AI model used
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- Not a replacement for professional security audits
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- May produce false positives or miss subtle issues
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- Always review AI suggestions before applying to production
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## Support
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For issues or questions, open an issue in the repository.
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## License
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MIT License - See LICENSE file for details
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## Week 4 Learning Objectives Met
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This project demonstrates mastery of all Week 4 skills:
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✅ **Multi-Model Integration** - Works with OpenAI, Anthropic, Google, Meta models
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✅ **Prompt Engineering** - Specialized prompts for different analysis types
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✅ **Code Analysis & Generation** - Security, performance, fixes, tests
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✅ **Gradio UI Development** - Multi-tab interface with streaming
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✅ **Real-World Application** - Addresses genuine developer needs
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✅ **Clean Architecture** - Modular, extensible, well-documented
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## What Makes This Novel?
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Compared to other Week 4 community contributions:
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| Feature | Other Projects | SecureCode AI |
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|---------|----------------|---------------|
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| Docstring Generation | ✅ (Many) | ➖ |
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| Code Conversion | ✅ (Many) | ➖ |
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| **Security Analysis** | ❌ None | ✅ **Unique** |
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| **Performance Profiling** | ❌ None | ✅ **Unique** |
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| **Educational Focus** | ❌ Limited | ✅ **Unique** |
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| Unit Test Generation | ✅ (Some) | ✅ Enhanced |
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| Auto-Fix with Explanation | ❌ None | ✅ **Unique** |
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**Result**: A production-ready tool that teaches secure coding while solving real problems!
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## Acknowledgments
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- **LLM Engineering Course** by Edward Donner
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- **OpenRouter** for multi-model API access
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- **Gradio** for the excellent UI framework
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- **OWASP** for security guidelines
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- **Community** for inspiration from Week 4 contributions
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## Contributing
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Ideas for enhancements:
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- Add more security rules (SANS Top 25, CWE)
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- Implement batch file processing
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- CI/CD integration (GitHub Actions)
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- VSCode extension
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- API endpoint for programmatic access
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- Support for more languages
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## License
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MIT License - See LICENSE file for details
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---
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**Built with ❤️ for developers who care about security and performance**
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