Emerging Threats and Countermeasures in Neuromorphic Systems: A Survey

2026-01-23

AI Summary

This work addresses emerging security threats in neuromorphic systems arising from brain-inspired architectures and memristive devices, particularly side-channel attacks and privacy breaches stemming from asynchronous event-driven mechanisms and stochastic device behavior. It presents the first systematic survey of security challenges in this domain and proposes a holistic hardware-software co-design framework that integrates spiking neural networks, memristor-based hardware, physically unclonable functions (PUFs), and true random number generators (TRNGs) for comprehensive security evaluation. Furthermore, the study introduces a trustworthy neuromorphic hardware design paradigm that balances energy efficiency with robust protection. By mapping the threat landscape of neuromorphic computing, this research establishes both theoretical foundations and practical guidelines for developing intrinsically secure, high-performance next-generation brain-inspired architectures.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• neuromorphic systems
• security threats
• spiking neural networks
• memristive devices
• side-channel vulnerabilities

Innovation

• neuromorphic computing
• spiking neural networks
• memristive devices
• hardware security
• Physical Unclonable Functions