Inferring Hidden User Models from the Behavior of Personalized LLM Agents
Researchers show that hidden user profiles built by personalised LLM agents can be inferred from observable behaviour, creating a privacy-leakage vector that data-minimisation alone may not prevent.
Summary written by editorial AI · Source link below
arXiv:2609.03815v1 Announce Type: new Abstract: Recent personalized LLM agents increasingly transform information retained in memory into compressed or structured representations, which we call user models, to guide later decisions. When source wording is removed from the state reachable through the ordinary interface, these models are commonly treated as more privacy-preserving because direct memory-extraction attacks lose the text they target. Yet we argue that user models expose a new attack
Editorial Analysis
Enterprises deploying personalised AI agents may unknowingly expose user data through inference attacks on compressed internal representations, complicating GDPR compliance claims.
Include user-model inference attacks in DPIAs for any personalised LLM agent deployment.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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