# Cascade: Token-Sharded Private LLM Inference

arXiv: https://arxiv.org/abs/2507.05228 · HTML: https://mlsanity.com/paper/2507.05228/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis](https://mlsanity.com/paper/2304.04675/) (2023)
- [MPCFormer: fast, performant and private Transformer inference with MPC](https://mlsanity.com/paper/2211.01452/) (2022)
- [PUMA: Secure Inference of LLaMA-7B in Five Minutes](https://mlsanity.com/paper/2307.12533/) (2023)
- [Nimbus: Secure and Efficient Two-Party Inference for Transformers](https://mlsanity.com/paper/2411.15707/) (2024)
- [PermLLM: Private Inference of Large Language Models within 3 Seconds under WAN](https://mlsanity.com/paper/2405.18744/) (2024)
- [Information Leakage from Embedding in Large Language Models](https://mlsanity.com/paper/2405.11916/) (2024)
- [Secure Transformer Inference Protocol](https://mlsanity.com/paper/2312.00025/) (2023)
- [CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference](https://mlsanity.com/paper/2412.10652/) (2024)
- [An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs](https://mlsanity.com/paper/2505.18332/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Graph-Based Analysis of Comparability, Scale, and Attribution in Private Transformer Inference](https://mlsanity.com/paper/2610.19607/) (2026)

## Abstract (arXiv)

As LLMs continue to increase in parameter size, the computational resources required to run them are available to fewer parties. Therefore, third-party inference services -- where LLMs are hosted by third parties with significant computational resources -- are becoming increasingly popular. However, third party inference raises critical concerns about user data privacy. To mitigate these risks, privacy researchers have developed provably secure schemes for third-party inference, such as Secure Multi-Party Computation (SMPC). However, SMPC protocols have significant computational and communication overhead, and do not scale to large models. In this work, we propose a new multi-party inference protocol, Cascade, that avoids these punitive costs by leveraging sharding in the sequence dimension to maintain privacy, trading off cryptographic privacy guarantees for increased performance and scalability. We demonstrate that Cascade is resistant to a generalization of a recent attack that is highly effective against other statistical privacy schemes, and that it is further resistant to learning-based attacks. As Cascade is orders of magnitude faster than existing schemes, our findings offer practical solutions for secure deployment of modern state-of-the-art LLMs.