# Emerging Threats and Countermeasures in Neuromorphic Systems: A Survey

arXiv: https://arxiv.org/abs/2601.16589 · HTML: https://mlsanity.com/paper/2601.16589/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 40 of its 174 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Builds on (most cited first, 30 of 40)

- [Membership Inference Attacks From First Principles](https://mlsanity.com/paper/2112.03570/) (2021)
- [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018)
- [Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack](https://mlsanity.com/paper/1811.09310/) (2018)
- [TBT: Targeted Neural Network Attack with Bit Trojan](https://mlsanity.com/paper/1909.05193/) (2019)
- [Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing](https://mlsanity.com/paper/1603.08270/) (2016)
- [HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise](https://mlsanity.com/paper/2110.11417/) (2021)
- [Spiking Deep Networks with LIF Neurons](https://mlsanity.com/paper/1510.08829/) (2015)
- [Brain-inspired computing: We need a master plan](https://mlsanity.com/paper/2104.14517/) (2021)
- [Memory and information processing in neuromorphic systems](https://mlsanity.com/paper/1506.03264/) (2015)
- [Spectre Attacks: Exploiting Speculative Execution](https://mlsanity.com/paper/1801.01203/) (2018)
- [DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories](https://mlsanity.com/paper/2111.04625/) (2021)
- [Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient](https://mlsanity.com/paper/2001.01587/) (2020)
- [Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters](https://mlsanity.com/paper/2012.05321/) (2020)
- [NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips](https://mlsanity.com/paper/2005.08041/) (2020)
- [Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data](https://mlsanity.com/paper/2302.06279/) (2023)
- [Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples](https://mlsanity.com/paper/2209.03358/) (2022)
- [Toward Robust Spiking Neural Network Against Adversarial Perturbation](https://mlsanity.com/paper/2205.01625/) (2022)
- [Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients](https://mlsanity.com/paper/2503.03272/) (2025)
- [BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks](https://mlsanity.com/paper/2402.00906/) (2024)
- [Side-channel attack analysis on in-memory computing architectures](https://mlsanity.com/paper/2209.02792/) (2022)
- [Event Encryption: Rethinking Privacy Exposure for Neuromorphic Imaging](https://mlsanity.com/paper/2306.03369/) (2023)
- [Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study](https://mlsanity.com/paper/2411.06613/) (2024)
- [Input-Triggered Hardware Trojan Attack on Spiking Neural Networks](https://mlsanity.com/paper/2503.21793/) (2025)
- [BlockHammer: Preventing RowHammer at Low Cost by Blacklisting Rapidly-Accessed DRAM Rows](https://mlsanity.com/paper/2102.05981/) (2021)
- [Mercury: An Automated Remote Side-channel Attack to Nvidia Deep Learning Accelerator](https://mlsanity.com/paper/2308.01193/) (2023)
- [Physical Unclonable Functions (PUF) for IoT Devices](https://mlsanity.com/paper/2205.08587/) (2022)
- [Membership Privacy Evaluation in Deep Spiking Neural Networks](https://mlsanity.com/paper/2409.19413/) (2024)
- [The Inherent Adversarial Robustness of Analog In-Memory Computing](https://mlsanity.com/paper/2411.07023/) (2024)
- [SCARE: Side Channel Attack on In-Memory Computing for Reverse Engineering](https://mlsanity.com/paper/2006.13095/) (2020)
- [PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators](https://mlsanity.com/paper/2304.11056/) (2023)

## Abstract (arXiv)

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.