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Spiking Neural Networks Are Harder to Steal: A Benchmark Study of Model Stealing Attacks

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 28/10/2026, 04:03:25 UTC.

Briefings

A new benchmark study from arXiv (2610.36298) compares model stealing attacks against Spiking Neural Networks (SNNs) and standard Artificial Neural Networks (ANNs). The research concludes that SNNs are consistently harder to steal than ANNs, with surrogates achieving 10-20% lower agreement and 15-25% lower transferability. This resistance is attributed to SNNs' sparse, time-dependent spike dynamics, which create decision boundaries that are difficult to approximate under large query budgets.

Key points

• SNNs achieve 10% to 20% lower agreement with target models compared to ANNs across all tested attacks and datasets.
• SNNs achieve 15% to 25% lower adversarial transferability than ANNs, making their functionality harder to replicate via black-box queries.

Context

This study benchmarks four specific model stealing attacks (Papernot, Tramer, T-RND, and color perturbation) against SNNs and their ANN counterparts on MNIST, Fashion-MNIST, and CIFAR-10 datasets. The findings extend existing security knowledge by demonstrating that SNNs' unique temporal computation methods provide inherent resistance to model replication attacks.

Limitations

The evidence is limited to four specific attacks and three datasets (MNIST, Fashion-MNIST, CIFAR-10). Results are presented as a benchmark study with no causal claims or causal claims made. The study does not provide a definitive security guarantee for all SNNs, only that they are harder to steal than ANNs under the tested conditions.

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