RFCLLM: Evaluating LLMs' Reasoning Ability of Network Protocol State Machines

2026-09-11

AI Summary

本文通过设计4个任务和1482个查询来评估大语言模型对网络协议状态机的理解能力，研究其在形式化表示中的准确性与影响因素。

Abstract

Mapping textual specifications into formal representations is essential for ensuring the correctness of protocol designs and implementations. LLM-generated mappings, used for networking security or testing, are assumed to capture a perfect understanding of the specification, which may not hold in practice. The goal of this paper is to assess the extent to which LLMs can interpret the specification correctly. We examine the degree to which an LLM's implicit representation of a finite-state transition system-defined via natural language descriptions-aligns with a manually generated ground-truth model. We designed 4 tasks and 1482 task queries for 16 protocols. We evaluated different judge biases, observed the inherent difficulty gaps between tasks, looked into the effect of 4 context types, and the influence of protocol characteristics. Our work contributes to a step toward verifying whether LLMs can really be trusted in FSM (Finite State Machine) reasoning of protocol specifications.

Problem

• LLMs
• Reasoning Ability
• Network Protocol
• Finite State Machine
• Specification

Innovation

• LLMs
• FSM Reasoning
• Protocol Specifications
• Finite-State Transition System