Subjects, Not Authors: The Authorship Hazard in Agentic Dataspaces

arXiv:2609.30614 · cs.CR, cs.AI, cs.DB, cs.MA · Submitted 2026-09-24 · Read on arXiv

cs.CR, cs.AI, cs.DB, cs.MA

Submitted: 2026-09-24

Updated: 2026-10-06

Comments: 16 pages, 3 figures, 13 tables

Project page: https://w3c.github.io/odrl/profile-dataspaces

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Provenance — 11× in this paper · explained in 3 episodes
• Ontology — 9× in this paper · explained in 2 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Graph Construction — 2× in this paper · explained in 3 episodes
• Knowledge Graph — 2× in this paper · explained in 7 episodes
• Model Context Protocol — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• ACM International Conference on Software Engineering — 1× in this paper
• AI Agents — 1× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Data Sovereignty — 1× in this paper · explained in 2 episodes
• Eclipse Dataspace Components — 1× in this paper
• Multi-Agent System — 1× in this paper · explained in 5 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• DAVE: A Policy-Enforcing LLM Spokesperson for Secure Multi-Document Data Sharing
• AgentODRL: A Large Language Model-based Multi-agent System for ODRL Generation
• From Instructions to ODRL Usage Policies: An Ontology Guided Approach
• Towards Automated Ontology Generation from Unstructured Text: A Multi-Agent LLM Approach
• LLM-empowered knowledge graph construction: A survey
• The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
• Progent: Securing AI Agents with Privilege Control
• Prompts Don't Protect: Architectural Enforcement via MCP Proxy for LLM Tool Access Control
• AIP: Agent Identity Protocol for Verifiable Delegation Across MCP and A2A
• Authentication and authorization in Data Spaces: A relationship-based access control approach for policy specification based on ODRL

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