arXiv:2606.06054v1 Announce Type: new Abstract: Personal AI agents increasingly rely on long-term memory to provide persistent personalization across sessions. However, existing memory pipelines are largely driven by semantic similarity: memory data close to the current query is retrieved and injected into the model context. This creates a critical trustworthiness gap, since a semantically related memory may still be contextually inappropriate, leading to threats such as cross-domain leakage, sycophancy, tool-call drift, or memory-induced jailbreaks. In this paper, we study memory search as a
The proliferation of personal AI agents and their reliance on long-term memory exposes critical vulnerabilities, necessitating immediate solutions for trustworthiness as their deployment scales.
This research addresses a fundamental weakness in current AI agent architectures, directly impacting their reliability, security, and ultimately, public trust and adoption.
The focus shifts from mere semantic similarity in memory retrieval to incorporating contextual appropriateness and trustworthiness, leading to more robust and secure AI agent design.
- · AI agents developers
- · AI security researchers
- · Enterprises deploying AI agents
- · Users of personal AI agents
- · Developers neglecting trustworthy AI practices
- · AI systems vulnerable to memory-induced errors
Improved reliability and reduced risks associated with personal AI agent interactions.
Accelerated adoption of AI agents in sensitive domains requiring high trust and data security.
New regulatory frameworks and industry standards emphasizing trustworthy AI memory management and context awareness.
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Read at arXiv cs.AI