Adversarial Attacks on Agentic AI Systems: Mechanisms, Impacts, and Defense Strategies

Pradipta Kishore Chakrabarty

Abstract: This study delves into the growing threat of adversarial attacks on agentic AI systems, highlighting their unique vulnerabilities owing to their complexity and expanded access privileges. Through theoretical and experimental analyses, it categorizes the attack vectors specific to these systems and evaluates their impacts. This study identifies novel attack surfaces beyond traditional AI vulnerabilities, particularly in systems with database access or critical decision-making capabilities [1]. This study proposes a multilayered defense framework to mitigate these threats, contributing significantly to agentic AI security. These insights are crucial for developing secure and trustworthy autonomous AI systems for rapidly evolving landscapes.

Keywords: Agentic AI, Agentic AI Security, Adversarial Attacks, Adversarial Threats, Autonomous Systems, AI Security, Defense Strategies, Threat Modeling, Adversarial Machine Learning, Cybersecurity, Attack Mitigation, AI Vulnerabilities, Prompt Injection, Agent Manipulation, Multilayered defense strategies