Eric Hilario, Sami Azam, Jawahar Sundaram, Khwaja Imran Mohammed, Bharanidharan Shanmugam
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引用次数: 0
Abstract
This paper examines the role of Generative AI (GenAI) and Large Language Models (LLMs) in penetration testing exploring the benefits, challenges, and risks associated with cyber security applications. Through the use of generative artificial intelligence, penetration testing becomes more creative, test environments are customised, and continuous learning and adaptation is achieved. We examined how GenAI (ChatGPT 3.5) helps penetration testers with options and suggestions during the five stages of penetration testing. The effectiveness of the GenAI tool was tested using a publicly available vulnerable machine from VulnHub. It was amazing how quickly they responded at each stage and provided better pentesting report. In this article, we discuss potential risks, unintended consequences, and uncontrolled AI development associated with pentesting.
期刊介绍：
The International Journal of Information Security is an English language periodical on research in information security which offers prompt publication of important technical work, whether theoretical, applicable, or related to implementation.
Coverage includes system security: intrusion detection, secure end systems, secure operating systems, database security, security infrastructures, security evaluation; network security: Internet security, firewalls, mobile security, security agents, protocols, anti-virus and anti-hacker measures; content protection: watermarking, software protection, tamper resistant software; applications: electronic commerce, government, health, telecommunications, mobility.
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