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Bifrost: Hybrid TEE-FHE Inference for Privacy-Preserving Transformer and LLM Serving

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arXiv:2606. 17421v1 Announce Type: new Abstract: Cloud-hosted transformer and large language model (LLM) inference creates a direct confidentiality problem: user prompts may contain sensitive code, business data, personal information, or regulated documents, yet remote serving exposes intermediate state to the cloud software stack and accelerator runtime.

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Authors: Chenghao Chen, Kailun Qin, Xiaolin Zhang, Chi Zhang, Dawu Gu
Released: 2026-06-17 (22d ago)
Topic: Related to current project
Relation to my work: Read next because Bifrost: Hybrid TEE-FHE Inference for Privacy-Preserving Transformer and LLM Serving overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, fill, persona, rect, soft, line, project. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2606.17421

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