Large Language Models for Blockchain Security: A Systematic Literature Review

2024-03-21

IACR Cryptology ePrint Archive

AI Summary

This study addresses the lack of clarity regarding the application of large language models (LLMs) in blockchain security and the absence of a systematic evaluation framework. We conduct a systematic literature review (SLR) to establish the first comprehensive research landscape—LLM4BS. Through topic modeling, cross-study comparison, and security capability mapping, we identify five core application areas: smart contract auditing, transaction anomaly detection, vulnerability repair, program analysis, and community governance. We synthesize seven recurring challenges, including scalability limitations, privacy leakage, and prompt injection attacks. Furthermore, we propose three future research directions: technical adaptation, trustworthy evaluation, and collaborative governance. The paper introduces a novel classification framework for LLM-empowered blockchain security and defines multi-dimensional evaluation criteria—encompassing functionality, robustness, interpretability, and compliance. This work provides researchers, developers, and regulators with both theoretical foundations and actionable guidelines for advancing secure, reliable, and responsible LLM integration in blockchain systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Multiagent Systems: Applications

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Large Language Models (LLMs) have emerged as powerful tools across various domains within cyber security. Notably, recent studies are increasingly exploring LLMs applied to the context of blockchain security (BS). However, there remains a gap in a comprehensive understanding regarding the full scope of applications, impacts, and potential constraints of LLMs on blockchain security. To fill this gap, we undertake a literature review focusing on the studies that apply LLMs in blockchain security (LLM4BS). Our study aims to comprehensively analyze and understand existing research, and elucidate how LLMs contribute to enhancing the security of blockchain systems. Through a thorough examination of existing literature, we delve into the integration of LLMs into various aspects of blockchain security. We explore the mechanisms through which LLMs can bolster blockchain security, including their applications in smart contract auditing, transaction anomaly detection, vulnerability repair, program analysis of smart contracts, and serving as participants in the cryptocurrency community. Furthermore, we assess the challenges and limitations associated with leveraging LLMs for enhancing blockchain security, considering factors such as scalability, privacy concerns, and ethical concerns. Our thorough review sheds light on the opportunities and potential risks of tasks on LLM4BS, providing valuable insights for researchers, practitioners, and policymakers alike.

Problem

• Understanding LLM applications in blockchain security
• Exploring LLM impacts on smart contract auditing
• Assessing challenges in LLM-based blockchain solutions

Innovation

• LLMs for smart contract auditing
• LLMs detect transaction anomalies
• LLMs analyze smart contract programs