RESEARCHInvestigateNEXT 12 MONTHS
Disclosure Begets Disclosure: A Self-Reinforcing Privacy Loop in LLM Agents with Persistent Memory
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Research shows LLM agents using persistent memory trigger a self-reinforcing loop where users disclose more personal data over time.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 21 October 2026
- Collected by OneBench
- 22 Oct 2026, 03:01 UK
Stored source excerpt
arXiv:2610.28315v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly use persistent memory to personalize responses, and personal attributes in this memory pose a…
Short excerpt from the collected text, not the full source. Use the source link to read it in context.
The factual summary is a OneBench synthesis, not a quotation or independent verification. Collection time is not publication time. Open the source for its full context; related reporting can share the same underlying announcement.
OneBench interpretation
Institutional assessment
So what
Agentic customer interactions using persistent memory can systematically escalate data privacy risks by prompting deeper user disclosures.
Do what
Review data governance and memory retention policies for customer-facing agentic LLM pilots.