LICET: Multi-Modal Physiological Human-Intent Verification for Autonomous AI Agent Authorization

Abstract

Autonomous AI agents executing consequential actions require authorization mechanisms that verify not only identity but voluntary intent. LICET (Latin: it is permitted) is a cryptographic middleware protocol binding AI agent authorization to real-time multi-modal physiological state via a three-layer architecture: (1) ECG waveform morphology matching as a medication-resistant identity and liveness anchor; (2) electrodermal activity (EDA) as a sympathetic cholinergic liveness signal immune to beta-adrenergic blockade; and (3) personalized Mahalanobis distance fusion over five physiological signals to elevate the cost of pharmacological coercion attacks. LICET defines a Biometric Trust Level hierarchy (L0-L3) aligned with the IETF RATS architecture (RFC 9334), per-event HKDF session-key derivation, HMAC biometric temporal signatures, Schnorr zero-knowledge proofs over BN128, and a SHA-256 hash-chained tamper-evident ledger. A reference implementation is publicly deployed at https://licet.dev/v1/.