Capture the Flag with ChatGPT: Security Testing with AI ChatBots

David Chamberlain, Ellis Casey
{"title":"Capture the Flag with ChatGPT: Security Testing with AI ChatBots","authors":"David Chamberlain, Ellis Casey","doi":"10.34190/iccws.19.1.2171","DOIUrl":null,"url":null,"abstract":"Penetration testing, commonly referred to as pen testing, is a process of assessing the security of a computer system or network by simulating an attack from an external or internal threat actor. One type of pen testing exercise that has become popular among cybersecurity enthusiasts is called Capture the Flag (CTF). This involves solving a series of challenges that simulate real-world hacking scenarios, with the goal of capturing a flag that represents a piece of sensitive information. Recently, there has been a growing interest in the use of natural language processing (NLP) and machine learning (ML) technologies for penetration testing and CTF exercises. One such technology that has received significant attention is ChatGPT, a large language model (LLM) trained by OpenAI based on the GPT-3.5 architecture. The use of ChatGPT in CTFs has several potential benefits for participants and organisers, including more dynamic and realistic scenarios and enhanced learning experiences, and enhance the effectiveness and realism of CTFs.. Future research can explore more sophisticated models and evaluate the effectiveness of ChatGPT in improving the performance of participants in CTFs.","PeriodicalId":429427,"journal":{"name":"International Conference on Cyber Warfare and Security","volume":"122 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Cyber Warfare and Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34190/iccws.19.1.2171","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Penetration testing, commonly referred to as pen testing, is a process of assessing the security of a computer system or network by simulating an attack from an external or internal threat actor. One type of pen testing exercise that has become popular among cybersecurity enthusiasts is called Capture the Flag (CTF). This involves solving a series of challenges that simulate real-world hacking scenarios, with the goal of capturing a flag that represents a piece of sensitive information. Recently, there has been a growing interest in the use of natural language processing (NLP) and machine learning (ML) technologies for penetration testing and CTF exercises. One such technology that has received significant attention is ChatGPT, a large language model (LLM) trained by OpenAI based on the GPT-3.5 architecture. The use of ChatGPT in CTFs has several potential benefits for participants and organisers, including more dynamic and realistic scenarios and enhanced learning experiences, and enhance the effectiveness and realism of CTFs.. Future research can explore more sophisticated models and evaluate the effectiveness of ChatGPT in improving the performance of participants in CTFs.
与 ChatGPT 一起夺旗:使用人工智能聊天机器人进行安全测试
渗透测试(通常称为笔测试)是通过模拟来自外部或内部威胁行为者的攻击来评估计算机系统或网络安全性的过程。在网络安全爱好者中流行的一种笔测试练习叫做 "夺旗"(CTF)。这包括解决一系列模拟真实世界黑客攻击场景的挑战,目标是夺取代表敏感信息的旗帜。最近,人们越来越关注将自然语言处理(NLP)和机器学习(ML)技术用于渗透测试和 CTF 演习。其中一项备受关注的技术是 ChatGPT,它是 OpenAI 基于 GPT-3.5 架构训练的大型语言模型(LLM)。在 CTF 中使用 ChatGPT 可为参与者和组织者带来多种潜在益处,包括更动态、更逼真的场景和更强的学习体验,以及增强 CTF 的有效性和真实性。未来的研究可以探索更复杂的模型,并评估 ChatGPT 在提高 CTF 参与者表现方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信