Adversarial Attacks on AI Security Systems
Shaista Alvi · 2025
As artificial intelligence (AI) becomes more integrated into security systems across industries, the possibility of hostile assaults increases, posing a substantial threat to both organizations and individuals. This chapter investigates the landscape of adversarial attacks against AI-powered security solutions, focusing on the consequences for data protection, anomaly detection, and overall system integrity. We delve into the sophisticated techniques employed by malicious actors to exploit vulnerabilities in models, including evasion attacks, poisoning attacks, and model inversion. The chapter also investigates emerging defense strategies, such as adversarial training, robust optimization, and ensemble methods, evaluating their effectiveness in fortifying AI security systems against evolving threats. Furthermore, we analyze the unique challenges faced by organizations in defending against these threats, given the rapid evolution of AI systems and the increasing complexity of attack vectors. By analyzing recent academic, the current status of adversarial AI security and suggestions for future study areas are investigated. This research augments towards more resilient AI security systems, hence improving the safety and dependability of intelligent protective measures in numerous applications and sectors. Through this investigation, it is proposed to bridge the gap between academia and industry, encouraging a better sympathetic of the intricate interplay between AI security and adversarial techniques in an increasingly linked digital ecosystem.