Trust, Accountability and Control: Post-Quantum Authentication and Evidence-Driven Authorization for Artificial Intelligence Agents

Artificial intelligence (AI) agents need authenticated identities, bounded action scopes and evidence before receiving adaptive authority. We present a post-quantum trust, accountability and control (TAC) protocol composing signed requests, independently signed observations, scoped admission and transactional evidence consumption. A Tamarin abstraction checks six universal receipt properties and two existential witnesses; three mutants expose omitted mechanisms. Replay preserves 38,400 recorded controller positions. Execution study E6 covers 48 matched configurations and eight ablations: the bound dispatcher produces no unpermitted effect in 22 mediated configurations, while both privileged bypasses succeed. Twelve-visit study E7 compares adaptive authority with competent static enforcement in 28 matched episodes, alongside 24 isolated execution ablations and two history-mutant diagnostics. Both policies resist tested command substitutions; adaptive warm-up withholds 24 of 48 compliant proposals. False authorized reports violate the illustrative task predicate under both. Two diagnostics show signed-prefix reconstruction detecting injected duplicate reinforcement before effects, and a separate arithmetic oracle checks retained trajectories. Paired-path medians are 3.41 ms for the module-lattice-based digital signature algorithm ML-DSA-44 and 2.19 ms for Ed25519 on the recorded x86 configuration; separate primitive experiments examine build sensitivity. Deterministic results establish conditional composition boundaries with progress costs. Truthful observation, complete mediation, provisioning and durable storage remain assumptions.

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Published
2026-11-01
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https://doi.org/10.3390/info17111083
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Access Control and Trust
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article

Trust, Accountability and Control: Post-Quantum Authentication and Evidence-Driven Authorization for Artificial Intelligence Agents

Vincenzo Loia, Domenico Furno
Information
Access Control and Trust
article

Trust, Accountability and Control: Post-Quantum Authentication and Evidence-Driven Authorization for Artificial Intelligence Agents

Vincenzo Loia, Domenico Furno
article en

Abstract

Artificial intelligence (AI) agents need authenticated identities, bounded action scopes and evidence before receiving adaptive authority. We present a post-quantum trust, accountability and control (TAC) protocol composing signed requests, independently signed observations, scoped admission and transactional evidence consumption. A Tamarin abstraction checks six universal receipt properties and two existential witnesses; three mutants expose omitted mechanisms. Replay preserves 38,400 recorded controller positions. Execution study E6 covers 48 matched configurations and eight ablations: the bound dispatcher produces no unpermitted effect in 22 mediated configurations, while both privileged bypasses succeed. Twelve-visit study E7 compares adaptive authority with competent static enforcement in 28 matched episodes, alongside 24 isolated execution ablations and two history-mutant diagnostics. Both policies resist tested command substitutions; adaptive warm-up withholds 24 of 48 compliant proposals. False authorized reports violate the illustrative task predicate under both. Two diagnostics show signed-prefix reconstruction detecting injected duplicate reinforcement before effects, and a separate arithmetic oracle checks retained trajectories. Paired-path medians are 3.41 ms for the module-lattice-based digital signature algorithm ML-DSA-44 and 2.19 ms for Ed25519 on the recorded x86 configuration; separate primitive experiments examine build sensitivity. Deterministic results establish conditional composition boundaries with progress costs. Truthful observation, complete mediation, provisioning and durable storage remain assumptions.

InformationVol. 17(11)
University of Salerno (IT)
Openalex Percentile: Top 5%
Access Control and Trust
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Trust, Accountability and Control: Post-Quantum Authentication and Evidence-Driven Authorization for Artificial Intelligence Agents — Vincenzo Loia, Domenico Furno · Information (2026) | TGRS Research Map | TGRS