  With the increase in software vulnerabilities that cause significant economic
and social losses, automatic vulnerability detection has become essential in
software development and maintenance. Recently, large language models (LLMs)
like GPT have received considerable attention due to their stunning
intelligence, and some studies consider using ChatGPT for vulnerability
detection. However, they do not fully consider the characteristics of LLMs,
since their designed questions to ChatGPT are simple without a specific prompt
design tailored for vulnerability detection. This paper launches a study on the
performance of software vulnerability detection using ChatGPT with different
prompt designs. Firstly, we complement previous work by applying various
improvements to the basic prompt. Moreover, we incorporate structural and
sequential auxiliary information to improve the prompt design. Besides, we
leverage ChatGPT's ability of memorizing multi-round dialogue to design
suitable prompts for vulnerability detection. We conduct extensive experiments
on two vulnerability datasets to demonstrate the effectiveness of
prompt-enhanced vulnerability detection using ChatGPT. We also analyze the
merit and demerit of using ChatGPT for vulnerability detection. Repository:
https://github.com/KDEGroup/LLMVulnerabilityDetection.
