Subjects, Not Authors: The Authorship Hazard in Agentic Dataspaces
On the map: LLM agents and tool use → Agent threats, guardrails and governance also fits: Web, search, and research agents
Abstract (arXiv)
Dataspace connectors decide whether a transfer may occur, not what the transferred value contains, tolerable for contracted applications, not for LLM agents that compose tool calls. Work on agents that generate governance artifacts evaluates output quality, not who may authorize an artifact for use. A published policy is what the decision point enforces, so publication is a governance event, and agents that are both policy subjects and policy authors write the norms that bind them. We name this the authorship hazard and state one principle: an agent is a subject of the governance plane, never an author of it. Its authorization channel to publication is closed by construction; its influence channel, drafting what humans approve, becomes an enforcement problem. Across 90 preregistered edits to the paper's running agreement, each evaluated on 344,512 requests, the six that only reclassify a field all change authorization and narrow a duty without touching policy text, and a policy-diff classifier passes all six. Read as worded, the privilege-delta conditions also pass 33 of 69 effective policy-text edits; read as covering any relaxation, none. Treating classification as authorship routes all six to review; the registry this requires is not yet built. At the execution boundary, protected fields reach the model in 105 of 105 cases under prompt-stated duties and in 0 of 105 under a compiled tool-call constraint, but values outside named fields are exposed in 7 of 7. At the review share measured, a central approval pool needs one approver per 20 to 138 participants.
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 18 of its 47 references that are in the map. First posted on arXiv 24 Sep 2026.
Builds on
18 of 47 references.
Closest ancestors · 7
Same direction, newest first
- 2026ActGov: Governing LLM Agent Actions via Policy-Constrained Validation21 Sep
- 2026Deontic Policies for Runtime Governance of Agentic AI Systems17 Jun
- 2026Overlaying Governance: A Compositional Authorization Framework for Delegation and Scope in Agentic AI2 Jun
- 2026AIP: Agent Identity Protocol for Verifiable Delegation Across MCP and A2A25 Mar
- 2025Progent: Securing AI Agents with Privilege Control16 Apr
- 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents24 Mar
- 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar
Other references · 11
- 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructionscited by 493
- 2022Taxonomy of Attacks on Open-Source Software Supply Chainscited by 58
- 2023Agent-OM: Leveraging LLM Agents for Ontology Matchingcited by 27
- 2025LLM-empowered knowledge graph construction: A surveycited by 21
- 2021Trojan Source: Invisible Vulnerabilitiescited by 8
- 2024Open Digital Rights Enforcement Framework (ODRE): from descriptive to enforceable policiescited by 5
- 2025From Instructions to ODRL Usage Policies: An Ontology Guided Approachcited by 2
- 2025AgentODRL: A Large Language Model-based Multi-agent System for ODRL Generationcited by 2
- 2026DAVE: A Policy-Enforcing LLM Spokesperson for Secure Multi-Document Data Sharingcited by 1
- 2026Towards Automated Ontology Generation from Unstructured Text: A Multi-Agent LLM Approachcited by 1
- 2026Prompts Don't Protect: Architectural Enforcement via MCP Proxy for LLM Tool Access Controlcited by 1
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