Towards Tackling Application Logic Flaws through Autonomous Formal-Logic Modeling and Automated Reasoning

2026-09-09

AI Summary

本文提出LL-Verifier框架，利用大型语言模型自动生成形式逻辑模型，并通过逻辑模型检查器进行严格推理，以解决应用程序中的逻辑缺陷问题。

Abstract

Logic flaws pose significant challenges in the design and implementation of modern, semantically rich systems and applications, impacting security, privacy, and trust. These flaws are inherently tied to business-specific semantics and threat models, making their discovery and reasoning difficult and hard to scale. Real-world systems often exhibit diverse application features, complex protocol logic, and domain-specific threat models, necessitating substantial human effort and domain expertise for effective security analysis. In this paper, we introduce LL-Verifier, a novel, automated framework for identifying logic vulnerabilities built on (1) large language models for autonomous modeling, and (2) logic model checkers for rigorous reasoning. LL-Verifier processes natural language inputs, in particular protocol descriptions and security goals, to automatically generate formal logic models and properties expressed in a new logic language built on a generic logic language Maude, optimized for modeling arbitrary application-level semantics. These formal models are then converted into logical state machines, enabling exhaustive, rigorous verification through logic level model checking. This approach streamlines the analysis of diverse, application-level protocols deployed in real-world scenarios, offering automated, exhaustive, and precise reasoning within their logical constraints. We evaluated the high effectiveness, efficiency, and practicality of LL-Verifier by applying it to 27 access control protocols of widely used IoT devices, which come with vendor-specific logic flows and semantics. While LL-verifier tackles a hard problem in application security, i.e., automatic logic flaws discovery, our analysis uncovers a range of sophisticated logic vulnerabilities in IoT protocols and devices with serious security and privacy implications.

Problem

• Logic Flaws
• Security Analysis
• Application Logic
• Automated Reasoning
• Formal-Logic Modeling

Innovation

• large language models
• logic model checkers
• automated reasoning
• formal logic models
• logical state machines