The adversarial AI landscape evolves at unprecedented speed as next-generation systems introduce entirely new attack surfaces that traditional security frameworks cannot adequately address. Foundation models processing natural language instructions create vulnerabilities through their instruction-following capabilities, multimodal architectures fusing cross-sensory information introduce cross-modal attack vectors, reinforcement learning (RL) agents adapting through environmental feedback become targets for reward manipulation, and quantum-enhanced systems threaten cryptographic foundations protecting AI deployments. This chapter equips you with analysis frameworks for foundation model vulnerabilities, including large language model (LLM) prompt injection and jailbreaking, RL exploitation methods, advanced multimodal attack orchestration, and quantum security implications for long-term AI system protection.