Proceedings

Large Language Models for Blockchain Security and Analytics: A Survey

Collette Eguakun Okundia, Cuneyt Akcora, Arijit Khan

Download BibTeX Cite

Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Survey Track. Pages 7976-7985.

https://doi.org/10.24963/ijcai.2026/885

Keywords

  • Data Mining: Mining graphs
  • Machine Learning: Supervised Learning
  • Data Mining: Applications

Abstract

Large Language Models (LLMs) are transforming blockchain security and analytics, yet a systematic evaluation of their capabilities remains limited. This survey provides a comprehensive, AI‑centric assessment of LLM‑based methods across over 70 recent studies spanning 11 application domains, such as security auditing, transaction fraud detection, and cryptocurrency portfolio management. Our unified taxonomy standardizes task formulations and evaluation practices to enable a comparison of six LLM roles across domains. For each domain, we review input representations tailored to blockchain data; LLM architectures, learning and inference paradigms, e.g., fine‑tuning, retrieval‑augmented generation, and agentic strategies. Our review analyzes the strengths, limitations, and emerging patterns of LLM roles observed in current systems. Finally, we provide practical guidance for selecting LLMs for specific roles and outline promising research directions.

Collette Eguakun Okundia, Cuneyt Akcora, and Arijit Khan. 2026. Large Language Models for Blockchain Security and Analytics: A Survey. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 7976–7985. https://doi.org/10.24963/ijcai.2026/885
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards