Regulatory gray areas of LLM Terms

TL;DR

This paper compares the Terms of Service of five major LLM providers, revealing variations and gray areas that create uncertainties for researchers and users, and provides a resource to navigate these regulations.

Contribution

It offers a comparative analysis of LLM Terms of Service, highlighting regulatory gray areas and providing a publicly available resource for better understanding.

Findings

Significant variation in usage restrictions across platforms

Identification of regulatory gray areas causing uncertainty

Implications for research and general use

Abstract

Large Language Models (LLMs) are increasingly integrated into academic research pipelines; however, the Terms of Service governing their use remain under-examined. We present a comparative analysis of the Terms of Service of five major LLM providers (Anthropic, DeepSeek, Google, OpenAI, and xAI) collected in November 2025. Our analysis reveals substantial variation in the stringency and specificity of usage restrictions for general users and researchers. We identify specific complexities for researchers in security research, computational social sciences, and psychological studies. We identify `regulatory gray areas' where Terms of Service create uncertainty for legitimate use. We contribute a publicly available resource comparing terms across platforms (OSF) and discuss implications for general users and researchers navigating this evolving landscape.