Large Language Models (LLMs) are increasingly utilized in digital libraries and knowledge systems to facilitate access to cultural and historical information. However, their outputs can reproduce subtle biases, particularly when addressing minority and low-resource communities. This study evaluates seven state-of-the-art LLMs on ten English prompts that embed culturally sensitive and potentially biased assumptions related to Vietnam, Myanmar, and Nepal. We systematically analyze these prompts’ responses for the presence of subtle bias, including gender stereotyping, linguistic ethnocentrism, epistemic bias, victim-blaming, and cultural essentialism. Our findings reveal significant variation in bias prevalence and type across models, with some exhibiting pervasive stereotyping and cultural marginalization, while others demonstrate more balanced and nuanced responses. These results emphasize the necessity for robust bias mitigation, culturally diverse training data, and human-in-the-loop oversight when deploying LLMs in digital heritage contexts. We discuss implications for ethical AI development in knowledge access and outline directions for future research to ensure fairness, transparency, and inclusivity in culturally sensitive AI applications.