New Benchmark ROK-FORTRESS Evaluates AI Safety in Geopolitical Contexts

Researchers have introduced ROK-FORTRESS, a bilingual benchmark for evaluating large language model (LLM) safety in National Security and Public Safety (NSPS) contexts, according to a preprint published on arXiv. The tool uses English-Korean language pairs and U.S.-South Korea geopolitical scenarios to measure how language and geopolitical factors interact in high-stakes AI applications.

Traditional multilingual safety benchmarks often rely on translation-only tests that preserve original scenarios without accounting for geopolitical nuances, the study notes. ROK-FORTRESS addresses this gap by incorporating real-world U.S.-ROK geopolitical dynamics into its evaluations. The dataset is hosted on Hugging Face.

The benchmark aims to improve understanding of how geopolitical context influences LLM outputs, particularly for systems deployed in international environments. With growing concerns about AI misuse in security-critical domains, the tool provides a framework to test models across language and political dimensions simultaneously.

“This work expands the scope of multilingual safety evaluations beyond technical translation accuracy to include geopolitical grounding,” the researchers wrote in the abstract of their paper (arXiv:2605.14152v1).