When Agentic Trust Crosses Organizational Boundaries: Structural Externalization and a Reference Model for Trust Evidence
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
Agentic systems increasingly invoke tools, services, data, and other agents across organizational boundaries, yet a relying party cannot assess a delegated action solely from producing-domain controls and records. This paper develops Trustworthiness as a Service (TaaS) through a synthesis of trustworthy-AI governance, agent security, distributed trust management, identity, provenance, assurance, and control-plane research. The analytical unit is a cross-domain reliance proposition that names the issuer, subject and action, relying party, administrative boundary, evidence dependencies, adverse condition, and required verification or adjudication semantics. The three-condition structural-externalization diagnostic identifies propositions that depend on multiple domains, require producer-independent reliance, and must remain reviewable after revocation, failure, conflicting records, or dispute. For such propositions, the paper specifies a trust-evidence envelope: an immutable workflow manifest linked to append-only, issuer-attributed attestations for task-scoped authority, policy and execution decisions, provenance, validity, disclosure, status, challenge, and recovery. A topology-neutral logical reference model assigns these functions to explicit roles and trust domains. Three analytical scenarios and the TaaS-Eval protocol proposal define manifests, independent consumers, hard gates, adversarial evidence tests, metrics, and reproducible artifact reporting. By composing established identity, authorization, provenance, assurance, and governance mechanisms around a bounded delegated action, TaaS provides a reusable profile for cross-domain reliance. It makes evidence dependencies, independent verification, challenge, and recovery explicit, supporting interoperable governance and future evaluation without treating producer assertions as ground truth.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 14 of its 33 references that are in the map. First posted on arXiv 19 Sep 2026.
Builds on
14 of 33 references.
Closest ancestors · 6
Same direction, newest first
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- 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar
Other references · 8
- 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 384
- 2024PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Modelscited by 267
- 2025"Show Me You Comply... Without Showing Me Anything": Zero-Knowledge Software Auditing for AI-Enabled Systemscited by 6
- 2024Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairnesscited by 3
- 2025Engineering Trustworthy Machine-Learning Operations with Zero-Knowledge Proofscited by 3
- 2026A Five-Plane Reference Architecture for Runtime Governance of Production AI Agentscited by 2
- 2025Control Plane as a Tool: A Scalable Design Pattern for Agentic AI Systemscited by 1
- 2026Trust-as-a-Service: Intelligent Collaboration Orchestration via Model Context Protocol-Aided Agentic AIcited by 1
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- 2026The Granularity Mismatch in Agent Security: Argument-Level Provenance Solves Enforcement and Isolates the LLM Reasoning Bottleneck