mlsanityData to 14 Oct 2026

NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips

arXiv 2005.08041 ↗First posted 16 May 2020cs.CR cs.LG stat.MLcited by 21

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Abstract (arXiv)

Due to their proven efficiency, machine-learning systems are deployed in a wide range of complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as a promising solution to the accuracy, resource-utilization, and energy-efficiency challenges in machine-learning systems. While these systems are going mainstream, they have inherent security and reliability issues. In this paper, we propose NeuroAttack, a cross-layer attack that threatens the SNNs integrity by exploiting low-level reliability issues through a high-level attack. Particularly, we trigger a fault-injection based sneaky hardware backdoor through a carefully crafted adversarial input noise. Our results on Deep Neural Networks (DNNs) and SNNs show a serious integrity threat to state-of-the art machine-learning techniques.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 May 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 4; 2023: 4; 2024: 4; 2025: 2; 2026: 2.

21 papers.

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  1. 2021Q-SpiNN: A Framework for Quantizing Spiking Neural Networkscited by 37
  2. 2022Hardly Perceptible Trojan Attack against Neural Networks with Bit Flipscited by 22
  3. 2021DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networkscited by 21
  4. 2021ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memoriescited by 20
  5. 2023Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Datacited by 18
  6. 2021Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Frameworkcited by 17
  7. 2023AdvART: Adversarial Art for Camouflaged Object Detection Attackscited by 12
  8. 2022What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradictioncited by 11
  9. 2023APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigationcited by 10
  10. 2021R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensorscited by 9
  11. 2024Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stackcited by 7
  12. 2022Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systemscited by 6
9 more
  1. 2024Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Studycited by 5
  2. 2025Input-Triggered Hardware Trojan Attack on Spiking Neural Networkscited by 5
  3. 2022Analysis of Power-Oriented Fault Injection Attacks on Spiking Neural Networkscited by 4
  4. 2024Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Camerascited by 3
  5. 2023Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferentcited by 0
  6. 2024SnatchML: Hijacking ML models without Training Accesscited by 0
  7. 2025On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learningcited by 0
  8. 2026Emerging Threats and Countermeasures in Neuromorphic Systems: A Surveycited by 0
  9. 2026Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attackscited by 0