Large language model (LLM) agents increasingly rely on persistent memory to personalize conversations. But the personal attributes stored in that memory can become a privacy liability if they are exposed. A new preprint on arXiv asks a pointed question: how does consulting that memory during a dialogue change what users are willing to share?
The paper, titled 'Disclosure Begets Disclosure: A Self-Reinforcing Privacy Loop in LLM Agents with Persistent Memory,' describes a potential feedback loop. If the agent draws on stored personal details, users may respond by offering even more sensitive information, which then gets added to memory, further increasing the risk of exposure. The abstract does not present specific results in the available excerpt, but the framing suggests the loop is a concern for designers of memory-enabled agents.
The authors do not appear to dispute the benefits of personalization; rather, they highlight the need to understand the privacy dynamics that persistent memory introduces. The preprint is available on arXiv under identifier 2610.28315.