Advanced Security Analysis Powered by Large Language Models and Semgrep
LLMGrep combines the precision of Semgrep's static analysis with the power of Large Language Models to deliver comprehensive security scanning, interactive vulnerability discussions, and intelligent rule generation capabilities.
Static Analysis | LLM Intelligence | Custom Rules |
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Semgrep-powered code scanning with pattern matching | AI-driven vulnerability assessment | Automated security rule generation |
Analysis Engine
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Intelligence Layer
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# Clone repository
git clone https://github.com/codebytemirza/LLMgrep.git
# Install dependencies
pip install -r requirements.txt
# Configure environment
cp .env.example .env
# Launch application
streamlit run app.py
Docker Deployment
docker build -t llmgrep .
docker run -p 8501:8501 --env-file .env llmgrep
Component | Technology | Purpose |
---|---|---|
Static Analysis | Semgrep | Pattern matching & vulnerability detection |
AI Engine | Groq LLM | Intelligent code analysis |
Interface | Streamlit | Interactive web application |
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Code Input
- Direct code entry
- File upload support
- Multi-file analysis
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Analysis Process
- Semgrep pattern scanning
- LLM-based code review
- Vulnerability assessment
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Results & Insights
- Detailed findings report
- Security recommendations
- Interactive consultation
Parameter | Description | Default |
---|---|---|
Model | LLM model selection | deepseek-r1 |
Temperature | Response variation | 0.1 |
Rules | Custom Semgrep rules | Optional |
Metrics | Performance tracking | Disabled |
# Setup development environment
python -m venv .venv
source .venv/bin/activate
# Install development dependencies
pip install -r requirements-dev.txt
We welcome contributions to LLMGrep. Please review our Contributing Guidelines before submitting pull requests.
Contribution Process
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Code Standards
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LLMGrep is released under the MIT License. See the LICENSE file for details.