AI RESEARCH
Information-Theoretic Privacy Control for Sequential Multi-Agent LLM Systems
arXiv CS.LG
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ArXi:2603.05520v1 Announce Type: cross Sequential multi-agent large language model (LLM) systems are increasingly deployed in sensitive domains such as healthcare, finance, and enterprise decision-making, where multiple specialized agents collaboratively process a single user request. Although individual agents may satisfy local privacy constraints, sensitive information can still be inferred through sequential composition and intermediate representations. In this work, we study \emph{compositional privacy leakage} in sequential LLM agent pipelines.