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Remembering More, Risking More: Longitudinal Safety Risks in Memory-Equipped LLM Agents

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Remembering More, Risking More: Longitudinal Safety Risks in Memory-Equipped LLM Agents
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The paper discusses the safety risks associated with memory-equipped LLM agents over time. It highlights the concept of temporal memory contamination, where accumulated memory from previous tasks can negatively impact the agent's performance in future tasks. The authors propose a new evaluation protocol to assess memory safety longitudinally rather than at a single point in time.

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arXiv cs.AI
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Computer Science > Artificial Intelligence arXiv:2605.17830 (cs) [Submitted on 18 May 2026] Title:Remembering More, Risking More: Longitudinal Safety Risks in Memory-Equipped LLM Agents Authors:Ahmad Al-Tawaha, Shangding Gu, Peizhi Niu, Ruoxi Jia, Ming Jin View a PDF of the paper titled Remembering More, Risking More: Longitudinal Safety Risks in Memory-Equipped LLM Agents, by Ahmad Al-Tawaha and 4 other authors View PDF HTML (experimental) Abstract:Safety evaluations of memory-equipped LLM agents typically measure within-task safety: whether an agent completes a single scenario safely, often under adversarial conditions such as prompt injection or memory poisoning.

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