English

Blockchain for Large Language Model Security and Safety: A Holistic Survey

Cryptography and Security 2024-11-19 v2 Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning

Abstract

With the growing development and deployment of large language models (LLMs) in both industrial and academic fields, their security and safety concerns have become increasingly critical. However, recent studies indicate that LLMs face numerous vulnerabilities, including data poisoning, prompt injections, and unauthorized data exposure, which conventional methods have struggled to address fully. In parallel, blockchain technology, known for its data immutability and decentralized structure, offers a promising foundation for safeguarding LLMs. In this survey, we aim to comprehensively assess how to leverage blockchain technology to enhance LLMs' security and safety. Besides, we propose a new taxonomy of blockchain for large language models (BC4LLMs) to systematically categorize related works in this emerging field. Our analysis includes novel frameworks and definitions to delineate security and safety in the context of BC4LLMs, highlighting potential research directions and challenges at this intersection. Through this study, we aim to stimulate targeted advancements in blockchain-integrated LLM security.

Keywords

Cite

@article{arxiv.2407.20181,
  title  = {Blockchain for Large Language Model Security and Safety: A Holistic Survey},
  author = {Caleb Geren and Amanda Board and Gaby G. Dagher and Tim Andersen and Jun Zhuang},
  journal= {arXiv preprint arXiv:2407.20181},
  year   = {2024}
}

Comments

Accepted to SIGKDD Explorations, to appear Dec 2024

R2 v1 2026-06-28T17:57:13.229Z