Understanding Stage-Wise Utility-Risk Trade-offs in LLM Agent Memory
Analysis of LLM agent memory reveals stage-specific privacy-utility trade-offs, giving designers a framework to limit data exposure without sacrificing personalisation.
Summary written by editorial AI · Source link below
arXiv:2608.30177v1 Announce Type: new Abstract: Long-term memory is becoming a core capability of LLM agents, enabling personalization and long-horizon interaction. However, memory mechanisms that retain, transform, or expose more information can affect both benign utility and susceptibility to memory poisoning. Existing evaluations typically measure memory utility or attack risk in isolation under fixed configurations, providing limited insight into how stage-specific design choices reshape th
Editorial Analysis
As persistent-memory agents handle personal data, GDPR-aligned enterprises need structured guidance on where memory mechanisms create privacy exposure.
Map your LLM agent memory architecture against the identified risk stages and enforce data-minimisation controls at each phase.
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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