DiverseFT: Diversifying Fault-Tolerant Systems using LLMs

2026-09-16

AI Summary

本文提出DiverseFT方法，利用大型语言模型自动生成多样化代码，以解决容错系统中因共享协议实现导致的共同缺陷问题。

Abstract

Fault-tolerant agreement protocols fail if replicas share a common flaw that simultaneously affects more replicas than the tolerable threshold. Therefore replicas should ideally fail independently, which can be achieved through diversification. However, in practice, often the same protocol implementation is shared by all replicas which is not surprising given that the provision of multiple diverse implementations is difficult and highly laborious. This poses a major risk, as a shared protocol implementation is a prime candidate for common bugs due to its complexity. With DiverseFT, we demonstrate how, given a reference implementation, Large Language Models (LLMs) can be utilised for the automated and scalable generation of code that compiles, passes tests, and crucially differs semantically/binary-wise, that can re- place code in the reference implementation, thereby significantly reducing diversification costs. We demonstrate the feasibility of diversification of replication protocol implementations using LLMs by diversifying three implementations: PBFT, HotStuff, and Raft, showing how up to 65% of the codebase can be diversified.

Problem

• fault-tolerant agreement protocols
• replicas
• diversification
• common bugs
• protocol implementation

Innovation

• Large Language Models
• Fault-Tolerant Systems
• Diversification
• Automated Code Generation