Information-Theoretic Privacy Control for Sequential Multi-Agent LLM Systems

• URL: http://arxiv.org/abs/2603.05520v1
• Date: Fri, 13 Feb 2026 18:23:03 GMT
• Title: Information-Theoretic Privacy Control for Sequential Multi-Agent LLM Systems
• Abstract summary: We study emphcompositional privacy leakage in sequential multi-agent large language model (LLM) systems.<n>Motivated by this analysis, we propose a privacy-regularized training framework that directly constrains information flow between agent outputs and agent-local sensitive variables.
• Score: 5.100622189286672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We formalize leakage using mutual information and derive a theoretical bound that characterizes how locally introduced leakage can amplify across agents under sequential execution. Motivated by this analysis, we propose a privacy-regularized training framework that directly constrains information flow between agent outputs and agent-local sensitive variables. We evaluate our approach across sequential agent pipelines of varying depth on three benchmark datasets, demonstrating stable optimization dynamics and consistent, interpretable privacy-utility trade-offs. Our results show that privacy in agentic LLM systems cannot be guaranteed by local constraints alone and must instead be treated as a system-level property during both training and deployment.

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