Adversarial Neural Inputs

Definition

Adversarial Neural Inputs examines inputs designed to mislead neural decoding, stimulation or safety controls within the broader domain of the protection of neural devices, signals, identities and interfaces from attack, compromise and unsafe failure.

Why this matters

Failures concerning inputs designed to mislead neural decoding, stimulation or safety controls can cause physical or psychological harm, loss of function, privacy invasion, identity compromise, exclusion, coercion or irreversible impact on human agency.

Research questions

Under which conditions can inputs designed to mislead neural decoding, stimulation or safety controls be delivered, measured or protected reliably, and how do outcomes vary by person, device, duration, context and governance controls?

Hypotheses

A transparent, safety-bounded and person-centred approach to adversarial neural inputs, combining validated measurement, informed consent, privacy and security controls, human oversight and longitudinal monitoring, will improve benefit–risk outcomes compared with opaque or technology-centred approaches.

Proposed methods

cybersecurity threat modelling; penetration testing; signal spoofing simulation; adversarial input testing; device-failure analysis; red-team exercises; incident-response drills; expert and affected-user review; reproducibility testing; methods adapted specifically to Adversarial Neural Inputs

Stakeholders and beneficiaries

neurotechnology users; clinicians; manufacturers; cybersecurity teams; regulators; hospitals; researchers; carers; emergency responders