arXiv:2611.05971 · v1

Enforcing Accountable Disclosure in Confidential AI

首次提交 2026-11-04 13:05

版本更新 2026-11-04 13:05

雷达观测 2026-11-05 11:10

摘要

Advances in CPU and GPU confidential computing enable confidential AI, allowing LLMs to process user prompts without exposing them to service providers or infrastructure operators. However, AI agents often need to send prompt-derived information to external services, and providers may also require limited disclosure for safety monitoring and failure diagnosis. We propose CORDE, a system for confidential virtual machines (CVMs) designed to satisfy the principle of Confine or Declare: user-derived data is confined by default, but when necessary, it may leave the CVM only after being "declared." To "declare" data is to record its exact contents, recipient, and stated reason in a user-accessible, tamper-evident declaration log. To put this principle into practice, we present an integrated system design that combines per-user information-flow control in Rust with a TEE-based ledger and remote attestation. Our evaluation on an NVIDIA H100 GPU shows that declaration before disclosure adds little overhead in task latency, about 0.5% on the AppWorld agent benchmark. CORDE provides a basis for accountable disclosure of user data, supporting responsible and transparent data management in AI services.