Disclosure Begets Disclosure: A Self-Reinforcing Privacy Loop in LLM Agents with Persistent Memory
Research shows LLM agents with persistent memory trigger a self-reinforcing loop in which users progressively disclose more sensitive data — a compounding privacy risk that static GDPR assessments may miss entirely.
Summary written by editorial AI · Source link below
arXiv:2610.28315v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly use persistent memory to personalize responses, and personal attributes in this memory pose a privacy risk if exposed. We ask how consulting memory during dialogue changes what users go on to say, and thus what the memory comes to reveal. In multi-session simulations, a simulated user with 24 personal attributes talks with a support agent that does or does not consult memory, and in both settings attr
Editorial Analysis
Enterprises deploying memory-enabled AI assistants face escalating privacy exposure as user trust compounds over interactions, potentially breaching data-minimisation principles.
Conduct a DPIA specifically addressing persistent-memory features in deployed LLM agents, including mechanisms to cap or purge accumulated personal data.
AI assistants that remember past conversations may inadvertently encourage users to share ever-more sensitive data, creating growing GDPR liability.
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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