Model Exposure on One Device Endangers Others: Rethinking Edge Neural Network Security across Devices
Cross-device model extraction attacks show that compromising a neural network on one edge platform can endanger deployments on entirely different hardware — widening the on-device ML threat model.
Summary written by editorial AI · Source link below
arXiv:2610.27152v1 Announce Type: new Abstract: Neural networks (NNs) are increasingly deployed on edge devices such as mobile and IoT platforms, offering low latency and improved privacy compared to cloud-based deployment. However, on-device models embedded in mobile applications can be unpacked and exposed to adversaries, enabling cross-node attacks in centrally distributed ecosystems, where adversarial examples crafted on one compromised device transfer effectively to other nodes sharing the
Editorial Analysis
Enterprises deploying the same ML models across heterogeneous edge fleets face compounded risk: a single device compromise can undermine model confidentiality fleet-wide.
Audit whether identical or similar model architectures are deployed across multiple edge device types and consider model diversification as a mitigation.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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