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New LLM frameworks tackle privacy-utility trade-offs in multi-user systems

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Researchers have developed new frameworks and methods to address privacy concerns in large language model (LLM) interactions. One approach, AIM, introduces a privacy-aware memory system for multi-agent, multi-user LLM environments, allowing for the dynamic classification of information as private or public to improve coordination while protecting sensitive data. Another study systematically analyzes the privacy-utility trade-off in LLM interactions, proposing an intent-driven framework that dynamically sanitizes data to preserve utility. Additionally, BodhiPromptShield offers a pre-inference mediation layer for LLM agent pipelines, detecting and replacing sensitive spans before they propagate through the system, significantly reducing residual exposure. AI

IMPACT These advancements in privacy-preserving LLM frameworks could enable wider adoption of AI in sensitive applications by mitigating data exposure risks.

RANK_REASON The cluster contains three research papers detailing new frameworks and methods for privacy in LLM systems.

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AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

New LLM frameworks tackle privacy-utility trade-offs in multi-user systems

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The cluster contains three research papers detailing new frameworks and methods for privacy in LLM systems.
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COVERAGE [5]

  1. arXiv cs.AI TIER_1 English(EN) · Guosen Wu, Huizhen Huang, Guoxiong Long, Tao Huang, Chen Hou ·

    ASLEval: Measuring Privacy Exposure Displacement in LLM Agent Sessions

    arXiv:2609.18864v1 Announce Type: cross Abstract: Privacy evaluations of tool-using LLM agents often inspect a designated action, final response, or attacker report. These local proxies can miss unauthorized exposure elsewhere in a multi-step session and lack common ground truth …

  2. arXiv cs.AI TIER_1 English(EN) · Minsun Shim, Ramisha Raida Karim, Ruthwik Jakkula, Kaiwen Zhou, Xin Liu, Xin Eric Wang, Zhou Li ·

    Confuse the Model, Control the Flow: Understanding and Mitigating Privacy Leakage from LLM Agents with Information Flow Control

    arXiv:2609.14003v1 Announce Type: cross Abstract: Personal AI agents built on large language models (LLMs) are increasingly given access to a user's private data and communications in order to provide personalized assistance. This access creates a persistent privacy risk: the age…

  3. arXiv cs.LG TIER_1 English(EN) · Zachary Johnson, Nigel Boachie Kumankumah, Somya Chatterjee, Tejas Sathyamurthi, Min Chen, Xinyi Alice Li, Xiao Wang, Emily Morgan Gelchie, Jessica Lin, Sadid A. Hasan, Sulaiman Vesal ·

    AIM: A Privacy-Aware Interoperable Memory Framework for Multi-Agent Multi-User LLM Systems

    arXiv:2609.12320v1 Announce Type: cross Abstract: Traditional large language models (LLMs) are scoped to individual user sessions, limiting their knowledge to a single conversation and preventing them from learning user preferences that evolve over time. Existing agentic memory s…

  4. arXiv cs.AI TIER_1 English(EN) · Zhenhua Liu, Zhanxu Xie, Junjie Yu, Tong Zhu, Lijun Li, Wenliang Chen ·

    Demystifying the Privacy-Utility Trade-off in LLM Interactions

    arXiv:2609.10992v1 Announce Type: new Abstract: The integration of Large Language Models into daily tasks relies on context-rich instructions, inevitably exposing sensitive user information. Current privacy-preserving methods typically employ context-agnostic static rules, causin…

  5. arXiv cs.CV TIER_1 English(EN) · Bo Ma, Jinsong Wu, Weiqi Yan ·

    BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines

    arXiv:2604.05793v2 Announce Type: replace-cross Abstract: In LLM agent pipelines, prompt privacy risk propagates beyond a single model call: raw user content enters retrieval queries, memory writes, tool arguments, OCR-derived text, and logs, and every downstream copy inherits wh…

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