SCRUTINEER: Detecting Logic-Level Usage Violations of Reusable Components in Smart Contracts

2025-11-14

AI Summary

Logical-layer misuse of Smart Contract Reusable components (SCRs)—where components comply with explicit syntactic rules yet violate business-context semantics—poses a critical security threat, demanding deep semantic understanding for detection. This paper presents the first automated system for identifying such logical-layer violations: it constructs a large language model–driven SCR knowledge base and employs retrieval-augmented generation (RAG) to extract implicit usage patterns; integrates composite feature modeling, snapshot-based reasoning for conflict detection, and fine-grained behavioral similarity analysis to enable context-aware logical behavior verification. Evaluated on three real-world datasets, the system achieves 80.77% precision, 82.35% recall, and 81.55% F1-score—substantially outperforming state-of-the-art approaches. It establishes the first semantics-driven framework for ensuring secure SCR reuse.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Logic Programming
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Cryptocurrency and smart contracts
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Smart Contract Reusable Components(SCRs) play a vital role in accelerating the development of business-specific contracts by promoting modularity and code reuse. However, the risks associated with SCR usage violations have become a growing concern. One particular type of SCR usage violation, known as a logic-level usage violation, is becoming especially harmful. This violation occurs when the SCR adheres to its specified usage rules but fails to align with the specific business logic of the current context, leading to significant vulnerabilities. Detecting such violations necessitates a deep semantic understanding of the contract's business logic, including the ability to extract implicit usage patterns and analyze fine-grained logical behaviors. To address these challenges, we propose SCRUTINEER, the first automated and practical system for detecting logic-level usage violations of SCRs. First, we design a composite feature extraction approach that produces three complementary feature representations, supporting subsequent analysis. We then introduce a Large Language Model-powered knowledge construction framework, which leverages comprehension-oriented prompts and domain-specific tools to extract logic-level usage and build the SCR knowledge base. Next, we develop a Retrieval-Augmented Generation-driven inspector, which combines a rapid retrieval strategy with both comprehensive and targeted analysis to identify potentially insecure logic-level usages. Finally, we implement a logic-level usage violation analysis engine that integrates a similarity-based checker and a snapshot-based inference conflict checker to enable accurate and robust detection. We evaluate SCRUTINEER from multiple perspectives on 3 ground-truth datasets. The results show that SCRUTINEER achieves a precision of 80.77%, a recall of 82.35%, and an F1-score of 81.55% in detecting logic-level usage violations of SCRs.

Problem

• Detecting logic-level usage violations in smart contract reusable components
• Addressing misalignment between component rules and business logic contexts
• Automating semantic analysis of implicit usage patterns and logical behaviors

Innovation

• Composite feature extraction for complementary representations
• LLM-powered knowledge construction with domain tools
• Retrieval-Augmented Generation-driven inspector for violation detection