This study addresses categories of harm surrounding Large Language Models (LLMs) in the field of artificial intelligence. It addresses five categories of harms addressed before, during, and after development of AI applications: pre-development, direct output, Misuse and Malicious Application, and downstream application. By underscoring the need to define risks of the current landscape to ensure accountability, transparency and navigating bias when adapting LLMs for practical applications. It proposes mitigation strategies and future directions for specific domains and a dynamic auditing system guiding responsible development and integration of LLMs in a standardized proposal.
@article{arxiv.2512.05929,
title = {LLM Harms: A Taxonomy and Discussion},
author = {Kevin Chen and Saleh Afroogh and Abhejay Murali and David Atkinson and Amit Dhurandhar and Junfeng Jiao},
journal= {arXiv preprint arXiv:2512.05929},
year = {2026}
}