Large Language Models for Blockchain Security and Analytics: A Survey

Abstract

Large Language Models are transforming blockchain security and analytics, yet systematic evaluation of their capabilities remains limited. This survey delivers a comprehensive, AI‑centric assessment of LLM‑based methods across more than sixty recent studies spanning nine application domains, including smart contract auditing, transaction fraud detection, cryptocurrency portfolio management, and DeFi security analysis. We introduce a unified taxonomy that standardizes task formulations, datasets, tools, algorithms, and evaluation practices, enabling consistent comparison across approaches. For each domain, we review deployed LLM architectures; learning and inference paradigms such as pre‑training, prompt engineering, fine‑tuning, retrieval‑augmented generation, and agentic strategies; and input representations tailored to blockchain data. We further analyze the strengths, limitations, and emerging patterns observed in current systems. Finally, the survey provides practical guidance for selecting LLM techniques and outlines promising research directions, e.g., explainable smart contract verification, automated DeFi protocol analysis, adversarial robustness evaluation, and scalable on‑chain anomaly detection.