The rapid development in large language models (LLMs) has transformed the landscape of natural language processing and understanding (NLP/NLU), offering significant benefits across various domains. However, when applied to scientific research, these powerful models exhibit critical failure modes related to scientific integrity and trustworthiness. Existing general-purpose LLM guardrails are insufficient to address these unique challenges in the scientific domain. We propose a comprehensive taxonomic framework for LLM guardrails encompassing four key dimensions: trustworthiness, ethics & bias, safety, and legal compliance. Our framework includes structured implementation guidelines for scientific research applications, incorporating white-box, blackbox, and gray-box methodologies. This approach specifically addresses critical challenges in scientific LLM deployment, including temporal sensitivity, knowledge contextualization, conflict resolution, and intellectual property protection.
