An AI Agent Execution Environment to Safeguard User Data
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arXiv:2607. 13718v1 Announce Type: cross Abstract: As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail.
arXiv:2609.14003v1 Announce Type: cross Abstract: Personal AI agents built on large language models (LLMs) are increasingly given access to a user's private data and communications in order to provid...
arXiv:2606. 26627v1 Announce Type: cross Abstract: Large language model agents increasingly query databases, search document collections, call external APIs, remember past interactions, and act on a user's behalf.
arXiv:2607. 05120v1 Announce Type: cross Abstract: AI agents act on behalf of user prompts, consuming external data and taking actions based on the agent context.
The paper argues that privacy in personalized AI should be viewed as a system-level issue rather than just a model-level one. It identifies four interconnected privacy‑risk channels in personalized AI and proposes four system‑level requirements—interaction trajectories, internal information flows, indirect leakage, and the privacy‑utility trade‑off—for evaluating privacy. The authors call for these requirements to be systematically incorporated into privacy audits of personalized AI systems.
arXiv:2609.35937v1 Announce Type: cross Abstract: While prior work has documented privacy failures in LLM agents, it remains unclear how the presentation of privacy guidance influences their choice o...