arXiv:2602.01146v2 Announce Type: replace Abstract: Conversational assistants are increasingly integrating long-term memory with large language models (LLMs). This persistence of memories, e.g., the user is vegetarian, can enhance personalization in future conversations. However, the same persistence can also introduce safety risks that have been largely overlooked. Hence, we introduce PersistBench to measure the extent of these safety risks. We identify two long-term memory-specific risks: cross-domain leakage, where LLMs inappropriately inject context from the long-term memories; and memory-
The increasing integration of long-term memory into conversational AI makes its safety implications, such as data leakage, a critical and immediate concern as these systems deploy at scale.
This research highlights a previously overlooked safety risk in advanced AI systems, demanding immediate attention from developers and regulators to prevent undesirable and potentially harmful outcomes.
The understanding of AI safety expands to include memory persistence as a critical vector for risk, necessitating new benchmarks and development practices for secure LLMs.
- · AI safety researchers
- · Developers focused on secure AI
- · Users prioritizing data privacy
- · LLM developers ignoring memory safety
- · Companies relying on insecure AI personalization
- · Models prone to cross-domain leakage
The new 'PersistBench' benchmark will drive the development of more robust and secure long-term memory systems for LLMs.
AI development pipelines will incorporate memory-specific safety evaluations, leading to more complex and regulated LLM deployment.
Enhanced memory safety standards could accelerate trust in AI agents, but also increase development costs and barriers to entry for smaller firms.
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Read at arXiv cs.AI