NeuroSnitch: Exploiting Inter-Spike Interval Statistics for Timing Side-Channel Attacks on Noisy Neuromorphic Systems

Neuromorphic computing promises energy-efficient solutions for embedded and edge systems, but introduces unique security challenges and a new attack surface. This paper presents NeuroSnitch, a first-ever timing side-channel attack to leverage subtle statistical variations in Inter-Spike Intervals (ISIs) on Spiking Neural Networks (SNNs) to extract secret information. We show that secret data, when modulating a neuron's input current, can be profiled through higher-order ISI statistics-mean, variance, skewness, and kurtosis-even under realistic noise sources, including observation noise, current fluctuation, and voltage jitter. Using the Leaky Integrateand-Fire (LIF) neuron model, we demonstrate that a Random Forest classifier can achieve 98.41% character-level classification accuracy on noisy ISI traces, enabling complete recovery of a 33-character secret string. This work exposes a previously underexplored and robust timing leakage vector in SNNs, underscoring the urgent need for tailored security measures in this emerging computing paradigm, particularly for sensitive embedded and IoT applications. Neuromorphic computing promises energy-efficient solutions for embedded and edge systems, but introduces unique security challenges and a new attack surface. This paper presents NeuroSnitch, a first-ever timing side-channel attack to leverage subtle statistical variations in Inter-Spike Intervals (ISIs) on Spiking Neural Networks (SNNs) to extract secret information. We show that secret data, when modulating a neuron's input current, can be profiled through higher-order ISI statistics-mean, variance, skewness, and kurtosis-even under realistic noise sources, including observation noise, current fluctuation, and voltage jitter. Using the Leaky Integrateand-Fire (LIF) neuron model, we demonstrate that a Random Forest classifier can achieve 98.41% character-level classification accuracy on noisy ISI traces, enabling complete recovery of a 33-character secret string. This work exposes a previously underexplored and robust timing leakage vector in SNNs, underscoring the urgent need for tailored security measures in this emerging computing paradigm, particularly for sensitive embedded and IoT applications.