AIM Intelligence Unveils 'XL-SafetyBench,' an AI Benchmark Reflecting the Cultures of 10 Countries

Goes beyond translation to measure legal, institutional, and cultural contexts across 10 countries—setting a new standard for global AI safety evaluation

SF, CA, UNITED STATES, June 5, 2026 /EINPresswire.com/ -- AI safety & security specialist AIM Intelligence (CEO Sangyoon Yu) has unveiled 'XL-SafetyBench,' a global benchmark designed to rigorously evaluate the reliability of large language models (LLMs) by reflecting the legal, institutional, and cultural contexts of countries around the world. Going beyond simple translation, it stands as the first global AI safety standard that precisely measures local risks and cultural nuances across 10 countries.

The core purpose of XL-SafetyBench is to measure how well leading LLMs understand legal, institutional, and cultural contexts. Covering 10 countries, including South Korea, the United States, India, Indonesia, France, Germany, Spain, and the UAE, the benchmark uses 5,500 localized test cases to assess 37 major LLMs on their grasp of these dimensions. Rather than simply checking whether a model can block harmful content, the goal is to determine whether the model actually recognizes the underlying risks, distinguish cases of the so-called "Illusion of Safety" (where a model appears safe by coincidence), and ultimately diagnose a model's readiness for global deployment. The "Illusion of Safety" refers to a phenomenon in which a model looks safe on paper because it refuses to answer, but in reality has simply evaded the question without recognizing the actual risk.

In practice, existing AI safety evaluations have largely relied on directly translating English-language prompts, leaving them unable to capture the legal, institutional, and cultural particularities of individual countries. Addressing this gap, XL-SafetyBench focuses on the fact that real-world AI risks manifest differently depending on each country's social structure, going beyond language barriers. It operates along two main tracks: the 'Local Risk Track' and the 'Cultural Sensitivity Track.'

The Local Risk Track evaluates a model's ability to handle risky requests based on each country's laws, fraud patterns, platforms, and social structures. The Cultural Sensitivity Track assesses whether a model can recognize region-specific cultural elements hidden within everyday requests and make appropriate ethical judgments. For example, the benchmark measures whether a model recognizes financial fraud related to Korea's jeonse (lump-sum lease deposit) system, or understands that recommending chrysanthemums as a gift in France is inappropriate because the flower symbolizes death and mourning, testing whether models truly grasp local institutions and cultural sentiments.

AIM Intelligence develops AI guardrails and AI red-teaming solutions across generative AI and LLMs, as well as image and vision models (VLMs), speech, multimodal systems, and physical AI. The company is particularly known for its advanced technology that simulates and blocks realistic attack scenarios designed to push models beyond a company's intended policies and use cases.

The project brought together 17 co-authors from 10 institutions, including global tech companies, domestic and international government research agencies, and leading universities, with participants from Microsoft, the Korea AI Safety Institute, KT, BMW Group, the Technical University of Munich, Ankara University, and Seoul National University. The combination of frontline experience from industry partners who operate AI in real-world global environments and the regulatory expertise of safety authorities in each country significantly enhanced the rigor of the evaluation framework.

Notably, Microsoft's AI Red Team led the initial direction of the research by raising the need for multicultural and multilingual safety evaluations. Microsoft contributed its accumulated know-how from real-world deployments of global AI models, strengthening the practical design of the evaluation criteria, while BMW Group supported the benchmark's development by sharing perspectives on the linguistic and cultural contexts of diverse global regions.

Myuhng-Joo Kim, Director of the Korea AI Safety Institute, emphasized, "AI safety evaluation can no longer rely solely on universal risk criteria — risks manifest differently depending on each country's laws, institutions, and cultural context. XL-SafetyBench is significant in that it incorporates these national contexts into the evaluation framework, pointing the way forward for global AI safety assessment."

KT, which was in charge of designing the benchmark's evaluation metrics, also shared a similar perspective. Jaehyung Park, Vice President of KT Frontier AI Lab, stated, "The key to this benchmark was designing the right evaluation metrics. We focused not just on whether a model produces answers that appear safe, but on capturing how the model actually behaves across diverse cultural contexts."

AIM Intelligence expects XL-SafetyBench to serve as a standard tool for verifying local adaptability and risk management when enterprises and public institutions adopt AI. The XL-SafetyBench paper is available on arXiv, and the dataset has been released on Hugging Face, allowing researchers and developers to use them freely.

Sangyoon Yu, CEO of AIM Intelligence, said, "True AI safety cannot stop at translated English tests — it begins with understanding how risks manifest in each country. We will continue to transform invisible local risks into measurable forms and set the standard for global AI deployment."

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