Uncovering Gaps Between RFC Updates and TCP/IP Implementations: LLM-Facilitated Differential Checks on Intermediate Representations

2025-10-28

AI Summary

Inconsistencies between TCP/IP protocol stack implementations and RFC specifications often lead to functional deviations and security vulnerabilities, yet existing detection methods lack support for multi-version, automated, and scalable conformance verification. This paper proposes the first automated framework integrating large language models (LLMs) with differential analysis. It first employs an LLM to parse RFC documents and extract protocol state machines, generating structured intermediate representations (IRs). Next, it models RFC evolution across versions and aligns kernel code semantics to enable incremental IR comparison. Finally, it precisely identifies functional discrepancies and potential vulnerabilities. Evaluated on mainstream kernels—including Linux and FreeBSD—the framework successfully uncovered multiple security defects arising from unsynchronized RFC updates. Our approach significantly advances the automation and scalability of protocol conformance verification.

Technology Category

• Natural Language Processing: Safety and Robustness
• Computer Vision: Language and Vision
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Social and technical mechanisms of refusal for web technologies and applications

Abstract

As the core of the Internet infrastructure, the TCP/IP protocol stack undertakes the task of network data transmission. However, due to the complexity of the protocol and the uncertainty of cross-layer interaction, there are often inconsistencies between the implementation of the protocol stack code and the RFC standard. This inconsistency may not only lead to differences in protocol functions but also cause serious security vulnerabilities. At present, with the continuous expansion of protocol stack functions and the rapid iteration of RFC documents, it is increasingly important to detect and fix these inconsistencies. With the rise of large language models, researchers have begun to explore how to extract protocol specifications from RFC documents through these models, including protocol stack modeling, state machine extraction, text ambiguity analysis, and other related content. However, existing methods rely on predefined patterns or rule-based approaches that fail to generalize across different protocol specifications. Automated and scalable detection of these inconsistencies remains a significant challenge. In this study, we propose an automated analysis framework based on LLM and differential models. By modeling the iterative relationship of the protocol and based on the iterative update relationship of the RFC standard, we perform incremental code function analysis on different versions of kernel code implementations to automatically perform code detection and vulnerability analysis. We conduct extensive evaluations to validate the effectiveness of our framework, demonstrating its effectiveness in identifying potential vulnerabilities caused by RFC code inconsistencies.

Problem

• Detecting inconsistencies between TCP/IP implementations and RFC standards
• Identifying security vulnerabilities from protocol specification mismatches
• Automating differential analysis of kernel code across RFC updates

Innovation

• LLM-based differential model for TCP/IP analysis
• Incremental code function analysis across RFC versions
• Automated vulnerability detection from protocol inconsistencies