Iranian Society of Cryptology The ISC International Journal of Information Security 2008-2045 Articles in Press 2026 01 01 EPT Benchmark: Evaluation of Persian Trustworthiness in Large Language Models 242935 10.22042/isecure.2026.242935 EN Mohammad Reza Mirbagheri Department of Computer Engineering, Sharif University of Technology, Tehran, Iran Seyed Mohammad Mahdi Mirkamali Department of Computer Engineering, Sharif University of Technology, Tehran, Iran Zahra Arani Department of Computer Engineering, Sharif University of Technology, Tehran, Iran Ali Javeri Department of Computer Engineering, Sharif University of Technology, Tehran, Iran Amir Mahdi Sadeghzadeh Mesgar Department of Computer Engineering, Sharif University of Technology, Tehran, Iran Rasool Jalili Department of Computer Engineering, Sharif University of Technology, Tehran, Iran Journal Article 2025 10 01 Large Language Models (LLMs), trained on extensive datasets using advanced deeplearning architectures, have demonstrated remarkable performance across a wide range of language tasks, becoming a cornerstone of modern AI technologies. However, ensuring their trustworthiness remains a critical challenge, asreliability is essential not only for accurate performance but also for upholding ethical, cultural, and social values. Careful alignment of training data and culturally grounded evaluation criteria is vital for developing responsible AI systems. In this study, we introduce the EPT (Evaluation of Persian Trustworthiness) metric, a culturally informed benchmark specifically designed to assess the trustworthiness of LLMs across six key aspects: Truthfulness, Safety, Fairness, Robustness, privacy, and ethical alignment. We curated a labelled dataset and evaluated the performance of several leading models—including ChatGPT, Claude, DeepSeek, Gemini, Grok, LLaMA, Mistral, and Qwen—using both automated LLM-based and human assessments. Our results reveal significant deficiencies in the safety dimension, underscoring the urgent need for focused attention on this critical aspect of model behaviour. Furthermore, our findings offer valuable insights into the alignment of these models with Persian ethical-cultural values and highlight critical gaps and opportunities for advancing trustworthy and culturally responsible AI. The dataset is publicly available at: https://github.com/Rezamirbagheri110/EPT-Benchmark. https://www.isecure-journal.com/article_242935_72c9108549781eccc8b107f269fe35bc.pdf