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arXiv · 2609.38249

SURE: Framework for Safety to Construct Trustworthy AI

Soeun Han·Jisoo Lee·Jeongyong Shim·Eunkyeong Lee·Eunmi Kim
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Abstract

Warning: This paper contains harmful and offensive text. Recently, large language models such as GPT-4, and Claude have revolutionized tasks in various domains. As the use of these large language models increases, people are increasingly concerned about AI safety and demand that large language models behave responsibly and safely. As a result, there has been growing global interest in developing methods to ensure AI safety. However, the detailed criteria for AI safety may vary depending on the country, culture, and policies of the company you serve. In this study, we propose SURE (A Safe and Unified AI Framework foR Everyone), which is designed as a framework for customizing the attributes of AI safety and ensuring the defined AI safety. Within SURE, we establish taxonomies for adversarial prompts that could threaten AI safety and construct prompts based on the taxonomies. We then define templates for desirable AI responses to these prompts and design an absolute safety scoring scheme. Finally, we conduct AI alignment using the datasets to gradually ensure AI safety. The effectiveness of SURE is demonstrated through experiments with various base models.

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Soeun Han, Jisoo Lee, Jeongyong Shim, Eunkyeong Lee, Eunmi Kim. 2026-10-01. SURE: Framework for Safety to Construct Trustworthy AI. https://arxiv.org/abs/2609.38249

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