Fuzzing the brain: Automated stress testing for the safety of ML-driven neurostimulation

TL;DR

This paper introduces an automated fuzzing approach to systematically test and identify unsafe neural stimulation patterns in ML-driven neuroprosthetic devices, enhancing safety assessment and regulatory compliance.

Contribution

It adapts coverage-guided fuzzing to neural stimulation, enabling empirical detection of safety violations in ML models used for neurostimulation.

Findings

Systematically reveals unsafe stimulation regimes exceeding safety limits.

Coverage metrics effectively compare unsafe output diversity across models.

Framework supports reproducible safety assessment for neural interfaces.

Abstract

Objective: Machine learning (ML) models are increasingly used to generate electrical stimulation patterns in neuroprosthetic devices such as visual prostheses. While these models promise precise and personalized control, they also introduce new safety risks when model outputs are delivered directly to neural tissue. We propose a systematic, quantitative approach to detect and characterize unsafe stimulation patterns in ML-driven neurostimulation systems. Approach: We adapt an automated software testing technique known as coverage-guided fuzzing to the domain of neural stimulation. Here, fuzzing performs stress testing by perturbing model inputs and tracking whether resulting stimulation violates biophysical limits on charge density, instantaneous current, or electrode co-activation. The framework treats encoders as black boxes and steers exploration with coverage metrics that quantify how broadly test cases span the space of possible outputs and violation types. Main results: Applied to deep stimulus encoders for the retina and cortex, the method systematically reveals diverse stimulation regimes that exceed established safety limits. Two violation-output coverage metrics identify the highest number and diversity of unsafe outputs, enabling interpretable comparisons across architectures and training strategies. Significance: Violation-focused fuzzing reframes safety assessment as an empirical, reproducible process. By transforming safety from a training heuristic into a measurable property of the deployed model, it establishes a foundation for evidence-based benchmarking, regulatory readiness, and ethical assurance in next-generation neural interfaces.