Papers › Secure Transformer Inference Protocol

Secure Transformer Inference Protocol

14 Nov 2023arXiv:2312.00025archive 2025-07-28

Mu Yuan, Lan Zhang, Xiang-Yang Li

Security of model parameters and user data is critical for Transformer-based services, such as ChatGPT. While recent strides in secure two-party protocols have successfully addressed security concerns in serving Transformer models, their adoption is practically infeasible due to the prohibitive cryptographic overheads involved. Drawing insights from our hands-on experience in developing two real-world Transformer-based services, we identify the inherent efficiency bottleneck in the two-party assumption. To overcome this limitation, we propose a novel three-party threat model. Within this framework, we design a semi-symmetric permutation-based protection scheme and present STIP, the first secure Transformer inference protocol without any inference accuracy loss. Experiments on representative Transformer models in real systems show that STIP has practical security and outperforms state-of-the-art secure two-party protocols in efficiency by millions of times.

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yuanmu97/secure-transformer-inference officialmentioned in paperpytorch report

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4 samples harvested; 4 ran; 0 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · violated contract
1ran · our draft was wrong
2ran · fixture could not drive it

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apply_rotary_emb yuanmu97/secure-transformer-inference/model.py official repository ran · fixture could not drive it MIT (permissive) · d7b6dcfe63bfe59b · report
permute_block yuanmu97/secure-transformer-inference/stip_llama.py official repository ran · our draft was wrong MIT (permissive) · fca57e9227bbfa47 · report
precompute_freqs_cis yuanmu97/secure-transformer-inference/model.py official repository ran · violated contract MIT (permissive) · 04a1fa63d6d4b8e4 · report
reshape_for_broadcast identical code first harvested elsewhere ran · fixture could not drive it licence of this copy not recorded · 5a639d78ada17fee · report

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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