Weaponizing AI in Cyberattacks A Comparative Study of AI powered Tools for Offensive Security

Offensive security, a critical aspect of modern cybersecurity, involves simulating cyberattacks to proactively identify and address vulnerabilities before they can be exploited. However, many tasks, such as network scanning and subdomain enumeration, are time-consuming and inefficient without automation, especially as cyberattacks grow increasingly sophisticated. Recent incidents, such as attacks targeting legacy communication systems like pagers and walkie-talkies, further underscore the urgency to automate offensive security operations. This paper presents a comparative study of automated tools, specifically WebCopilot and Sublist3r for subdomain enumeration, and RustScan combined with Nmap compared to Nmap alone for network scanning. The results demonstrate that automation significantly reduces the time required for these tasks, enhancing both efficiency and effectiveness in offensive security practices. Additionally, this study explores the potential of artificial intelligence (AI) to further transform offensive security by automating more complex tasks, such as simulating attack scenarios and adapting dynamically to defenses. Positioned as a proposal, this research advocates for the integration of AI into the attack life cycle, highlighting the future potential of fully automating offensive security processes to create more intelligent, adaptive, and effective tools.