Agent Mosaic: Recognizing Confidentiality Leaks in Agentic LLM Systems

Daniel Ruiz, Luca Luceri, Emilio Ferrara · 2026 · arXiv

DOI: 10.48550/arXiv.2610.30418 · open-access PDF

Abstract

Modern LLM agents can retrieve information from accessible documents, and then disclose a sensitive conclusion assembled from them. This mosaic effect creates a critical risk for agents operating over confidential data. We quantify its realization with 30 synthetic cybersecurity, medical, and military examples, yielding 10,800 final answers from three LLM agents in controlled simulation and 1,440 answers from live agents in two open-source harnesses. Even under benign queries designed to be answerable from one document, GPT-5.6-Sol, the best performing model tested, discloses forbidden composi

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