MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge Translation

2025-02-24

AI Summary

Smart contract bytecode vulnerability detection faces two key challenges: high-noise instructions obscuring semantic signals and incomplete semantics due to missing data/control flow. To address these, we propose a multi-teacher collaborative knowledge distillation framework featuring two novel mechanisms: (1) an instruction denoising teacher that filters irrelevant bytecode operations, and (2) a neuron-level distillation-driven semantic complementation teacher that recovers source-code–level semantics—both modeling knowledge transfer as a regression task. We further integrate abstract vulnerability pattern modeling, optimized bytecode embedding, and cross-modal alignment between source code and bytecode. Evaluated on 229,000 real-world smart contracts, our method achieves a mean F1-score of 92.7% across four prevalent vulnerability types, significantly outperforming state-of-the-art approaches.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Cryptocurrency and smart contracts
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Smart contracts, closely intertwined with cryptocurrency transactions, have sparked widespread concerns about considerable financial losses of security issues. To counteract this, a variety of tools have been developed to identify vulnerability in smart contract. However, they fail to overcome two challenges at the same time when faced with smart contract bytecode: (i) strong interference caused by enormous non-relevant instructions; (ii) missing semantics of bytecode due to incomplete data and control flow dependencies. In this paper, we propose a multi-teacher based bytecode vulnerability detection method, namely extbf{M}ulti- extbf{T}eacher extbf{V}ulnerability extbf{Hunter} ( extbf{MTVHunter}), which delivers effective denoising and missing semantic to bytecode under multi-teacher guidance. Specifically, we first propose an instruction denoising teacher to eliminate noise interference by abstract vulnerability pattern and further reflect in contract embeddings. Secondly, we design a novel semantic complementary teacher with neuron distillation, which effectively extracts necessary semantic from source code to replenish the bytecode. Particularly, the proposed neuron distillation accelerate this semantic filling by turning the knowledge transition into a regression task. We conduct experiments on 229,178 real-world smart contracts that concerns four types of common vulnerabilities. Extensive experiments show MTVHunter achieves significantly performance gains over state-of-the-art approaches.

Problem

• Detects vulnerabilities in smart contracts
• Overcomes bytecode interference and missing semantics
• Utilizes multi-teacher knowledge translation for effective denoising and semantic completion

Innovation

• Denoising instruction patterns
• Semantic extraction via neuron distillation
• Multi-teacher guided vulnerability detection