A Survey on Offensive AI Within Cybersecurity

As AI takes on pivotal roles in essential applications, like self-driving vehicles, healthcare diagnosis, and financial services, it becomes a tempting target for malicious actors [16]. This study aims to comprehensively explore the realm of offensive AI, shedding light on its multifaceted dimensions, the techniques involved, its consequences, and potential future implications. Cyberattacks have surged in both complexity and frequency. This is evidenced by the escalating costs associated with data breaches. In 2022, businesses incurred an average loss of $4.35 million, an increase of $0.11 million from the previous year and a 12.7% rise from 2020 [22]. Moreover, the volume of data breaches has reached historic highs, with approximately 15 million records exposed during the third quarter of 2022. Furthermore, the third quarter of 2022 witnessed an alarming 57,116 distributed denial-of-service (DDoS) attacks [78]. Against this backdrop, understanding and mitigating security risks in machine learning (ML) has emerged as a pivotal aspect of cybersecurity.