Agent Mosaic measures confidentiality leaks from multi-document inference
Original title: Agent Mosaic: Recognizing Confidentiality Leaks in Agentic LLM Systems
GPT-5.6-Sol disclosed forbidden composites in 7.08% of simulated answers to benign single-document queries.
Researchers tested whether LLM agents assemble sensitive conclusions from separately accessible documents, a mosaic effect. They used 30 synthetic cybersecurity, medical, and military examples, producing 10,800 simulated answers from three agents and 1,440 live answers in two open-source harnesses. Even the best model, GPT-5.6-Sol, leaked forbidden composites at a non-negligible rate, and visible classification headers plus aggregation rules did not stop unsafe disclosures. Some refusals themselves revealed the prohibited conclusion.
Unconfirmed: Results are from synthetic tasks and two open-source harnesses, not production deployments.
- Simulation disclosure for GPT-5.6-Sol was 7.08% (95% CI: 3.75-10.67%).
- Live harnesses had worse disclosure rates than the controlled simulation.
- With safeguards, unsafe disclosures remained in 32.37% of simulated answers.
- The CI for that pooled rate is 28.33-36.19% across all intents.
From the original article
1 min read at huggingface.co
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.
By Daniel Ruiz, Luca Luceri, Emilio Ferrara · huggingface.co
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