LLM Code Analyzer: A Code Security Detection Tool Combining Large Language Models and Static Analysis

LLM Code Analyzer is an open-source intelligent code analysis system that combines large language models (LLMs) and static analysis technologies. It supports multi-language vulnerability detection, attack surface identification, and security repair suggestions, and offers both online (relying on OpenAI API) and offline (local models like CodeLlama) operation modes, aiming to address the pain points of traditional code security detection.

Traditional static analysis tools have a high false positive rate and are difficult to detect complex logic vulnerabilities; manual audits have high accuracy but are costly and hard to scale. Relying solely on LLMs has issues such as hallucinations, insufficient domain knowledge, and high reasoning costs. LLM Code Analyzer solves this contradiction through a hybrid solution (AI + static analysis).

Core Design: Dual-mode architecture (online/offline), multi-language support (Python/JS/Java/C++/PHP), structured output (vulnerability details, attack surface, repair suggestions, etc.).

Technical Implementation: Front-end and back-end separation architecture (FastAPI backend + pure front-end); static analysis module identifies common vulnerabilities like SQL injection and command injection based on rule matching; LLM module performs semantic analysis through prompt engineering; analysis orchestrator coordinates the two to merge results.

The tool can be applied to security left-shift in the development phase (pre-commit detection), security audit assistance (filtering high-risk areas), security training (vulnerability explanations and repair examples), and legacy code evaluation (quickly assessing security status), effectively reducing repair costs and improving audit efficiency.

LLM Code Analyzer represents a beneficial attempt at AI-assisted code security detection. By combining LLM semantic understanding and static analysis reliability, it provides developers with an intelligent and practical security tool. With the advancement of LLM technology, hybrid solutions are expected to become standard practice in the code security field.

Limitations: Dependence on external services (OpenAI/Ollama), limited analysis depth for large projects, and AI analysis still has false positives.

Improvement Directions: Support more programming languages, integrate into CI/CD pipelines, provide IDE plugins, and introduce code graph neural network technology.