Neuromorphic computing systems have significantly advanced in various real-world applications, such as object recognition, robotics, and autonomous vehicles. Designers typically leverage large-scale models on dedicated hardware platforms like FPGAs, GPUs, or ASICs to develop these emerging systems. However, collecting datasets, training models, and designing accelerators to maintain privacy and reliability requires substantial time and effort. As neuromorphic systems become increasingly complex, hardware implementations face considerable vulnerabilities. Without knowing these accelerators’ internal structures and designs, attackers can still reverse-engineer the neural networks by exploiting various side-channel information. Furthermore, due to the complexity of neuromorphic systems, which integrate numerous neurons and synapses, the accumulation of fault probability presents a growing threat to system reliability. Therefore, ensuring fault tolerance and reliability becomes paramount in the design of these systems. This chapter examines the primary threats to the reliability of neuromorphic systems, highlighting the importance of fault-tolerance mechanisms, and discusses several recovery methods to mitigate these risks, ensuring that neuromorphic systems can operate reliably in the face of various challenges and potential attacks.