# Prompt-Enhanced Software Vulnerability Detection Using ChatGPT

arXiv: https://arxiv.org/abs/2308.12697 · HTML: https://mlsanity.com/paper/2308.12697/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2023.

## Built on by (most cited first, 30 of 51)

- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024)
- [Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG](https://mlsanity.com/paper/2406.11147/) (2024)
- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [Agents in Software Engineering: Survey, Landscape, and Vision](https://mlsanity.com/paper/2409.09030/) (2024)
- [Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning](https://mlsanity.com/paper/2406.03718/) (2024)
- [How Far Have We Gone in Vulnerability Detection Using Large Language Models](https://mlsanity.com/paper/2311.12420/) (2023)
- [How secure is AI-generated Code: A Large-Scale Comparison of Large Language Models](https://mlsanity.com/paper/2404.18353/) (2024)
- [KNighter: Transforming Static Analysis with LLM-Synthesized Checkers](https://mlsanity.com/paper/2503.09002/) (2025)
- [Let the Trial Begin: A Mock-Court Approach to Vulnerability Detection using LLM-Based Agents](https://mlsanity.com/paper/2505.10961/) (2025)
- [Multi-role Consensus through LLMs Discussions for Vulnerability Detection](https://mlsanity.com/paper/2403.14274/) (2024)
- [To Err is Machine: Vulnerability Detection Challenges LLM Reasoning](https://mlsanity.com/paper/2403.17218/) (2024)
- [Harnessing Large Language Models for Software Vulnerability Detection: A Comprehensive Benchmarking Study](https://mlsanity.com/paper/2405.15614/) (2024)
- [MdEval: Massively Multilingual Code Debugging](https://mlsanity.com/paper/2411.02310/) (2024)
- [LLbezpeky: Leveraging Large Language Models for Vulnerability Detection](https://mlsanity.com/paper/2401.01269/) (2024)
- [A Contemporary Survey of Large Language Model Assisted Program Analysis](https://mlsanity.com/paper/2502.18474/) (2025)
- [From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security](https://mlsanity.com/paper/2412.15004/) (2024)
- [Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT](https://mlsanity.com/paper/2404.17110/) (2024)
- [VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection](https://mlsanity.com/paper/2509.11523/) (2025)
- [Agent That Debugs: Dynamic State-Guided Vulnerability Repair](https://mlsanity.com/paper/2504.07634/) (2025)
- [An Insight into Security Code Review with LLMs: Capabilities, Obstacles, and Influential Factors](https://mlsanity.com/paper/2401.16310/) (2024)
- [CoDefeater: Using LLMs To Find Defeaters in Assurance Cases](https://mlsanity.com/paper/2407.13717/) (2024)
- [Large Language Models for In-File Vulnerability Localization Can Be "Lost in the End"](https://mlsanity.com/paper/2502.06898/) (2025)
- [IFEvalCode: Controlled Code Generation](https://mlsanity.com/paper/2507.22462/) (2025)
- [RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models](https://mlsanity.com/paper/2509.04078/) (2025)
- [Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap](https://mlsanity.com/paper/2512.04680/) (2025)
- [A Systematic Study of Code Obfuscation Against LLM-based Vulnerability Detection](https://mlsanity.com/paper/2512.16538/) (2025)
- [Improving LLM Reasoning for Vulnerability Detection via Group Relative Policy Optimization](https://mlsanity.com/paper/2507.03051/) (2025)
- [VulInstruct: Teaching LLMs Root-Cause Reasoning for Vulnerability Detection via Security Specifications](https://mlsanity.com/paper/2511.04014/) (2025)
- [From SFT to RL: Demystifying the Post-Training Pipeline for LLM-based Vulnerability Detection](https://mlsanity.com/paper/2602.14012/) (2026)

## Abstract (arXiv)

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.