After the OpenAI Debacle, a Case for Trusted Execution Environments for Frontier LLM Inference
Michael_D_Moor · x · 2026-09-09
Following the recent OpenAI privacy debacle, MichaelDMoor proposes a broadly adopted trusted execution environment (TEE) for frontier LLM inference:
- User prompts stay private, invisible to the model provider
- Model weights stay private to the provider
- Both decrypt only if the software hash matches a trusted stack, verifiable independently by both user and model company — no third-party trust required
He flags the core open problem: how to stop users from attacking the model (jailbreaks, distillation) when the provider can't monitor prompts or outputs — all defenses would have to live inside the private model+harness. An open engineering discussion on privacy-preserving inference infrastructure.
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