Neuromorphic computing mimics brain-inspired mechanisms through spiking neurons and energy-efficient processing, offering a pathway to efficient in-memory computing (IMC). However, these advancements raise critical security and privacy concerns. As the adoption of bio-inspired architectures and memristive devices increases, so does the urgency to assess the vulnerability of these emerging technologies to hardware and software attacks. Emerging architectures introduce new attack surfaces, particularly due to asynchronous, event-driven processing and stochastic device behavior. The integration of memristors into neuromorphic hardware and software implementations in spiking neural networks offers diverse possibilities for advanced computing architectures, including their role in security-aware applications. This survey systematically analyzes the security landscape of neuromorphic systems, covering attack methodologies, side-channel vulnerabilities, and countermeasures. We focus on both hardware and software concerns relevant to spiking neural networks (SNNs) and hardware primitives, such as Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs) for cryptographic and secure computation applications. We approach this analysis from diverse perspectives, from attack methodologies to countermeasure strategies that integrate efficiency and protection in brain-inspired hardware. This review not only maps the current landscape of security threats but provides a foundation for developing secure and trustworthy neuromorphic architectures.

翻译：神经形态计算通过脉冲神经元和能效处理模拟脑启发机制，为实现高效存内计算提供了一条途径。然而，这些进展引发了关键的安全与隐私问题。随着仿生架构和忆阻器件的应用日益广泛，评估这些新兴技术对硬件和软件攻击的脆弱性变得愈发紧迫。新兴架构引入了新的攻击面，这尤其源于异步事件驱动处理与随机器件行为。将忆阻器集成到神经形态硬件及脉冲神经网络软件实现中，为先进计算架构（包括其在安全感知应用中的作用）提供了多样化的可能性。本综述系统分析了神经形态系统的安全态势，涵盖攻击方法、侧信道漏洞及防护对策。我们聚焦于与脉冲神经网络相关的硬件与软件问题，以及适用于密码学和安全计算应用的硬件原语，如物理不可克隆函数和真随机数生成器。我们从多重视角展开分析，从攻击方法到兼顾效率与保护的仿生硬件防护策略。本综述不仅描绘了当前安全威胁的格局，更为开发安全可信的神经形态架构奠定了基础。