Adversarial Neural Inputs

1. Hypothesis

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.

2. Experiment design

Design: adversarial neurosecurity assurance programme combining laboratory testing, simulated compromise and operational recovery exercises focused on Adversarial Neural Inputs 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 Independent variables: technology type; exposure or intervention intensity; duration; assurance controls; human oversight; user characteristics; operating context Dependent variables: functional outcome; biological or neural safety; user agency; privacy; reliability; recovery; subtopic-specific outcome for inputs designed to mislead neural decoding, stimulation or safety controls Confounders: age; health; disability; medication; prior experience; baseline physiology; environment; device quality; clinician or operator expertise; socioeconomic access Measures: attack success rate; detection latency; signal integrity; device availability; safety impact; recovery time; residual risk; patch effectiveness; validated subtopic measures for inputs designed to mislead neural decoding, stimulation or safety controls; subgroup effects; false-positive and false-negative rates; user-reported burden Success criteria: Statistically and clinically or practically meaningful benefit; acceptable adverse-event profile; preserved agency and privacy; no disproportionate subgroup harm; reproducible performance; explicit safety limits; effective recovery and human escalation. Failure conditions: No meaningful benefit; biological, neural, psychological, privacy or rights harm exceeds benefit; performance fails outside narrow conditions; unsafe dependency emerges; consent or refusal is compromised; incidents cannot be detected, reversed or remediated.

3. Seed result / current evidence

DEFENSIBLE SEED RESULT — NON-EMPIRICAL. The current evidence supports Adversarial Neural Inputs as a testable research proposition. Problem basis: Connected neurotechnology introduces attack surfaces that can affect confidentiality, device behaviour, physiological safety, identity and trust. The specific unresolved issue is inputs designed to mislead neural decoding, stimulation or safety controls. Directional expectation: If supported, the proposed approach should improve functional outcome; biological or neural safety; user agency; privacy; reliability; recovery; subtopic-specific outcome for inputs designed to mislead neural decoding, stimulation or safety controls while reducing adverse effects, misuse, exclusion, dependency and recovery time. Proposed observations: attack success rate; detection latency; signal integrity; device availability; safety impact; recovery time; residual risk; patch effectiveness; validated subtopic measures for inputs designed to mislead neural decoding, stimulation or safety controls; subgroup effects; false-positive and false-negative rates; user-reported burden. Seed data profile: Evidence Strength 10/100; Confidence 25/100; Maturity 20/100; Overall Health 35/100; Novelty 80/100; Strategic Importance 95/100. Evidence boundary: No validated results yet.; experiments 0, studies 0, participants 0. This is suitable for protocol formation and baseline comparison, not as a finding of effect.

4. Seed conclusion

DEFENSIBLE SEED CONCLUSION — PROVISIONAL. Adversarial Neural Inputs warrants structured testing because the CSV identifies a defined problem, falsifiable hypothesis, measurable outcomes and relevant literature foundations. The present position is that “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.” is plausible and decision-relevant, but unvalidated. Proceed to controlled testing against the stated success and failure conditions. Confirm, narrow or reject this seed after effect sizes, uncertainty, subgroup outcomes, adverse effects, persistence and handback performance are observed.

Prior-art search performed before starting

PRIOR-ART SEED BASELINE — PARTIAL. The CSV records these literature domains: Neuroscience; neurotechnology; physiology; medical-device safety; rehabilitation; cybersecurity; bioethics; human rights; literature specific to Adversarial Neural Inputs. It also records: NIST Cybersecurity Framework — https://www.nist.gov/cyberframework; FDA medical-device cybersecurity guidance — https://www.fda.gov/; CISA medical-device security resources — https://www.cisa.gov/; MITRE ATT&CK — https://attack.mitre.org/. Evidence register status: “Seeded; authoritative source register initiated; empirical evidence not yet ingested”. This is defensible as a starting prior-art inventory, but not as proof of a completed systematic search because search dates, databases, exact queries, reviewer, result counts, screening decisions, claim mapping and a replayable receipt are absent.

Prior-art material named: Existing literature: Neuroscience; neurotechnology; physiology; medical-device safety; rehabilitation; cybersecurity; bioethics; human rights; literature specific to Adversarial Neural Inputs. References: NIST Cybersecurity Framework — https://www.nist.gov/cyberframework; FDA medical-device cybersecurity guidance — https://www.fda.gov/; CISA medical-device security resources — https://www.cisa.gov/; MITRE ATT&CK — https://attack.mitre.org/

Critical gap / next action

Create and attach a dated prior-art search log; lock the protocol; execute the proposed study; link raw data and analysis; then replace the results and conclusion placeholders with evidence-bounded findings.