LLM vs. SAST: A Technical Analysis on Detecting Coding Bugs of GPT4-Advanced Data Analysis

TL;DR

This paper compares GPT-4's effectiveness to traditional SAST tools in detecting software vulnerabilities, demonstrating GPT-4's superior accuracy and discussing security considerations for LLMs in vulnerability scanning.

Contribution

It provides a comprehensive analysis of GPT-4's capabilities in security vulnerability detection, highlighting its outperformance over SAST and addressing security best practices.

Findings

GPT-4 achieves 94% accuracy in vulnerability detection.

GPT-4 outperforms SAST across 32 vulnerability types.

Security concerns of LLMs are discussed with recommended best practices.

Abstract

With the rapid advancements in Natural Language Processing (NLP), large language models (LLMs) like GPT-4 have gained significant traction in diverse applications, including security vulnerability scanning. This paper investigates the efficacy of GPT-4 in identifying software vulnerabilities compared to traditional Static Application Security Testing (SAST) tools. Drawing from an array of security mistakes, our analysis underscores the potent capabilities of GPT-4 in LLM-enhanced vulnerability scanning. We unveiled that GPT-4 (Advanced Data Analysis) outperforms SAST by an accuracy of 94% in detecting 32 types of exploitable vulnerabilities. This study also addresses the potential security concerns surrounding LLMs, emphasising the imperative of security by design/default and other security best practices for AI.