As AI systems and large language models (LLMs) become integrated into decision-making, communication, and automation workflows, their security becomes a pressing concern. Despite their performance, these models have vulnerabilities that can be exploited through adversarial techniques like prompt manipulation, data exploitation, and cyber-attacks. These exploits undermine system reliability while posing risks to privacy, misinformation, and safety. Examining the vulnerabilities of AI and LLMs, alongside methods used to exploit them, may further reveal limitations of current models and help develop more resilient, trustworthy AI systems. Examining Vulnerabilities and Adversarial Exploitation of AI and LLMs explores AI security, bridging governance, policy, compliance, and zero-trust strategy with AI-driven defense, detection, and engineering. It examines LLM vulnerabilities and security models, addressing responsible AI adoption, data privacy compliance, and global policy alignment. This book covers topics such as prompt manipulation, threat detection, and AI governance, and is a useful resource for engineers, policymakers, academicians, researchers, and scientists.