Adapting Large Language Models for Smart Contract Defects Detection in the Open Network Blockchain

Abstract

Smart contracts on the open network (TON) have become vital in Internet of Things (IoT) applications due to their low latency and high scalability. However, the unique architectural features of TON introduce specialized vulnerabilities that existing tools fail to address comprehensively. In this letter, we propose a novel defect detection framework that combines large language models (LLMs) for automated defect discovery with a locatable call graph for precise and efficient code analysis. Our method identifies four new types of TON-specific defects: 1) Ignore Errors Mode Usage; 2) Premature Acceptance; 3) Pseudo Deletion; and 4) Improper Jetton Refund. Evaluated on 1640 real-world smart contracts written in FunC and Tact, the framework uncovers 669 defects, with an average of one defect every 2.45 code segments. The detection achieves an average F1 score of 99.75% for FunC and 100% for Tact contracts. Additionally, our approach demonstrates lightweight computational overhead, consuming only 12.6 MB of memory and achieving a mean response time of 0.05 s. These results highlight the accuracy, efficiency, and practicality of our framework for securing TON-based smart contracts in IoT ecosystems.