RealVul: Can We Detect Vulnerabilities in Web Applications with LLM?

TL;DR

RealVul is a novel LLM-based framework tailored for detecting PHP vulnerabilities, employing advanced sample extraction and data synthesis techniques to enhance detection accuracy and generalization across diverse codebases.

Contribution

The paper introduces the first LLM framework specifically for PHP vulnerability detection, addressing data scarcity and sample extraction challenges.

Findings

Significant improvement in detection effectiveness.

Enhanced generalization across multiple PHP projects.

Outperforms existing vulnerability detection methods.

Abstract

The latest advancements in large language models (LLMs) have sparked interest in their potential for software vulnerability detection. However, there is currently a lack of research specifically focused on vulnerabilities in the PHP language, and challenges in extracting samples and processing persist, hindering the model's ability to effectively capture the characteristics of specific vulnerabilities. In this paper, we present RealVul, the first LLM-based framework designed for PHP vulnerability detection, addressing these issues. By vulnerability candidate detection methods and employing techniques such as normalization, we can isolate potential vulnerability triggers while streamlining the code and eliminating unnecessary semantic information, enabling the model to better understand and learn from the generated vulnerability samples. We also address the issue of insufficient PHP vulnerability samples by improving data synthesis methods. To evaluate RealVul's performance, we conduct an extensive analysis using five distinct code LLMs on vulnerability data from 180 PHP projects. The results demonstrate a significant improvement in both effectiveness and generalization compared to existing methods, effectively boosting the vulnerability detection capabilities of these models.