Practice and Evaluation of Code Vulnerability Detection Using Large Language Models

This article introduces an open-source project for code vulnerability detection based on large language models (LLMs). The project uses the arag0rn/SecVulEval dataset to evaluate the ability of various LLMs to identify security vulnerabilities, providing developers with a practical reference solution for security detection. The core goal of the project is to verify whether current LLMs have the ability to accurately identify code security vulnerabilities and provide quantifiable reference data through a standardized evaluation process.

With the continuous increase in the complexity of software systems, security vulnerability detection has become a key link in the software development process. Traditional manual code auditing methods are inefficient and costly, while rule-based security scanning tools often have problems such as high false positive rates and difficulty in detecting new types of vulnerabilities. In recent years, large language models (LLMs) have shown strong capabilities in code understanding and generation, providing a new technical path for automated vulnerability detection.

Project Overview

code-vulnerability-detection is an open-source project focused on evaluating the code vulnerability detection capabilities of large language models. Developed by MohamedYasserOaf, it systematically tests the security vulnerability identification performance of various mainstream LLMs based on the SecVulEval dataset. Its core goal is to answer whether current LLMs have the ability to accurately identify code vulnerabilities and provide quantifiable reference data for security researchers and developers.

Technical Architecture

The project adopts a modular architecture, including the following components:

• Dataset Integration: Uses the arag0rn/SecVulEval benchmark dataset, which contains labeled samples of various common vulnerabilities (such as buffer overflow, SQL injection, etc.) from real open-source projects.
• Model Evaluation Framework: Supports batch evaluation of multiple LLMs (LangChain-integrated models, local open-source models, cloud API services) and uses a unified prompt template to ensure result comparability.
• Result Analysis Module: Provides tools for saving original responses, accuracy statistics, performance analysis of vulnerability type classification, and visualization display.

Through systematic experiments, the project reveals important characteristics of LLMs in the field of code security detection:

1. LLMs have have a certain ability to identify common security vulnerability patterns, especially types that appear frequently in training data, indicating that they can learn security pattern features from massive code;
2. Detection performance is related to vulnerability types: semantic simple vulnerabilities (such as hard-coded credentials) have high accuracy, while complex vulnerabilities (such as race conditions) have limited performance;
3. There are limitations to relying solely on LLMs: outputs may be uncertain, and the ability to detect zero-day vulnerabilities is significantly reduced.

Practical significance: Provides data support for security research, development, and model improvement.

Application Scenarios

• Security Research Teams: Use as a benchmark testing tool to evaluate new models or compare the effects of prompt strategies;
• Development Teams: Use as a supplementary link in code review for preliminary screening before manual auditing to improve efficiency;
• Model Developers: Understand the weak links of models and improve training data or architecture in a targeted manner.

Usage Recommendations

Currently, large language models are more suitable as auxiliary tools. It is recommended to combine them with traditional static analysis tools to form a multi-level detection system.

With the evolution of LLM technology, the field of code security detection is expected to see innovations: multi-modal models can process both code and natural language security documents simultaneously, providing a comprehensive analysis perspective; agent-based automated security audit systems are exploring the realization of a complete closed loop from vulnerability detection to repair recommendations. The open-source contribution of this project provides experimental data and evaluation frameworks for the community, promoting the standardized application of LLMs in the security field.