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

Mara Downing; Matthew Peng; Jacob Granley; Michael Beyeler; Tevfik Bultan

doi:10.1088/1741-2552/ae4927

February 23, 2026Journal of Neural EngineeringOpen Access

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

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Key Points

The aim is to enhance safety evaluations for machine learning models in neurostimulation through a new empirical approach.
Used violation-focused fuzzing to assess safety of deployed models.
Transformed safety into a measurable property instead of a training heuristic.
Established evidence-based benchmarking processes.
Provided a reproducible method for safety assessments.
Enabled readiness for regulatory evaluations of neural interfaces.
Highlighted the importance of empirical safety measures in technology deployment.

Implication

Automated assessments improve safety in neurostimulation technologies, suggesting readiness for regulatory standards.

Abstract

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.

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Cite This Study

Downing et al. (2026) studied this question.

synapsesocial.com/papers/699fe24b95ddcd3a253e62b4 https://doi.org/10.1088/1741-2552/ae4927

View Full Paper

Ask AI

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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