We investigate the paradigm of adversarial attacks that target the emergent dynamics of Complex Adaptive Smart Cities (CASCs). To facilitate the analysis of such attacks, we develop quantitative definitions and metrics of attack, vulnerability, and resilience in the context of CASC security. Furthermore, we propose multiple schemes for classification of attack surfaces and vectors in CASC, complemented with examples of practical attacks. Building on this foundation, we propose a framework based on reinforcement learning for simulation and analysis of attacks on CASC, and demonstrate its performance through two real-world case studies of targeting power grids and traffic management systems. We also remark on future research directions in analysis and design of secure smart cities and complex adaptive systems.