mlsanityData to 14 Oct 2026

Emerging Threats and Countermeasures in Neuromorphic Systems: A Survey

arXiv 2601.16589 ↗First posted 23 Jan 2026cs.CR cs.AI cs.ETcited by 0

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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.

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.

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40 of 174 references.

References · 40

  1. 2021Membership Inference Attacks From First Principlescited by 878
  2. 2018Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooringcited by 357
  3. 2018Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attackcited by 125
  4. 2019TBT: Targeted Neural Network Attack with Bit Trojancited by 113
  5. 2016Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computingcited by 70
  6. 2021HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noisecited by 65
  7. 2015Spiking Deep Networks with LIF Neuronscited by 63
  8. 2021Brain-inspired computing: We need a master plancited by 51
  9. 2015Memory and information processing in neuromorphic systemscited by 48
  10. 2018Spectre Attacks: Exploiting Speculative Executioncited by 47
  11. 2021DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memoriescited by 42
  12. 2020Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradientcited by 31
28 more
  1. 2020Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameterscited by 27
  2. 2020NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flipscited by 21
  3. 2023Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Datacited by 18
  4. 2022Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examplescited by 13
  5. 2022Toward Robust Spiking Neural Network Against Adversarial Perturbationcited by 12
  6. 2025Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradientscited by 9
  7. 2024BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attackscited by 7
  8. 2022Side-channel attack analysis on in-memory computing architecturescited by 6
  9. 2023Event Encryption: Rethinking Privacy Exposure for Neuromorphic Imagingcited by 6
  10. 2024Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Studycited by 5
  11. 2025Input-Triggered Hardware Trojan Attack on Spiking Neural Networkscited by 5
  12. 2021BlockHammer: Preventing RowHammer at Low Cost by Blacklisting Rapidly-Accessed DRAM Rowscited by 4
  13. 2023Mercury: An Automated Remote Side-channel Attack to Nvidia Deep Learning Acceleratorcited by 4
  14. 2022Physical Unclonable Functions (PUF) for IoT Devicescited by 3
  15. 2024Membership Privacy Evaluation in Deep Spiking Neural Networkscited by 3
  16. 2024The Inherent Adversarial Robustness of Analog In-Memory Computingcited by 3
  17. 2020SCARE: Side Channel Attack on In-Memory Computing for Reverse Engineeringcited by 2
  18. 2023PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Acceleratorscited by 2
  19. 2024NEUROSEC: FPGA-Based Neuromorphic Audio Securitycited by 2
  20. 2024Robust and Efficient Adversarial Defense in SNNs via Image Purification and Joint Detectioncited by 2
  21. 2024SNNGX: Securing Spiking Neural Networks with Genetic XOR Encryption on RRAM-based Neuromorphic Acceleratorcited by 2
  22. 2023Improving Reliability of Spiking Neural Networks through Fault Aware Threshold Voltage Optimizationcited by 1
  23. 2023Variation Enhanced Attacks Against RRAM-based Neuromorphic Computing Systemcited by 1
  24. 2023Power-balanced Memristive Cryptographic Implementation Against Side Channel Attackscited by 1
  25. 2024CoMeT: Count-Min-Sketch-based Row Tracking to Mitigate RowHammer at Low Costcited by 1
  26. 2024Error Detection and Correction Codes for Safe In-Memory Computationscited by 1
  27. 2025Understanding the Security Landscape of Embedded Non-Volatile Memories: A Comprehensive Surveycited by 1
  28. 2025Unsupervised Backdoor Detection and Mitigation for Spiking Neural Networkscited by 1

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