This book provides a deep exploration of the AI-driven cyber threat landscape, covering inherent risks across Large Language Models (LLMs), Generative AI applications, agentic systems, and the underlying AI infrastructure. It expands beyond traditional analysis by examining how prompt engineering, autonomous agents, and multi-model orchestration introduce new attack surfaces, including prompt injection, tool abuse, data leakage, and agent manipulation. Drawing on real-world case studies, the book presents hands-on, technical methodologies for identifying vulnerabilities across AI pipelines from model behavior to system-level integrations. In addition, the book delivers a comprehensive treatment of defensive strategies and secure design patterns for modern AI systems, including LLMs, GenAI applications, and agentic architectures. It introduces a structured framework that begins with foundational AI and system concepts such as prompt injection and autonomous agents, progresses through practical assessment techniques grounded in adversarial thinking, and culminates in robust security controls and mitigation strategies. By bridging AI and cybersecurity, the book equips IT professionals, security practitioners, and business leaders with the knowledge required to understand, evaluate, and defend against evolving AI-powered cyber threats in increasingly autonomous and interconnected environments.
