PlanTwin: Privacy-Preserving Planning Abstractions for Cloud-Assisted LLM Agents
Cloud-hosted large language models (LLMs) have become the de facto planners in agentic systems, coordinating tools and guiding execution over local environments. In many deployments, however, the environment being planned over is private, containing source code, files, credentials, and metadata that cannot be exposed to the cloud. Existing solutions address adjacent concerns, such as execution isolation, access control, or confidential inference, but they do not control what cloud planners observe during planning: within the permitted scope, \textit{raw environment state is still exposed}. We introduce PlanTwin, a privacy-preserving architecture for cloud-assisted planning without exposin...
| Authors | Guangsheng Yu, Qin Wang, Rui Lang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 19 March 2026 |
| Last updated at source | 20 March 2026 |
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