Michael Michie

Akriti Vij, Benjamin Chua, Darshini Ramiah et al.

As frontier AI models are deployed globally, it is essential that their behaviour remains safe and reliable across diverse linguistic and cultural contexts. To examine how current model safeguards hold up in such settings, participants from the International Network for Advanced AI Measurement, Evaluation and Science, including representatives from Singapore, Japan, Australia, Canada, the EU, France, Kenya, South Korea and the UK conducted a joint multilingual evaluation exercise. Led by Singapore AISI, two open-weight models were tested across ten languages spanning high and low resourced groups: Cantonese English, Farsi, French, Japanese, Korean, Kiswahili, Malay, Mandarin Chinese and Telugu. Over 6,000 newly translated prompts were evaluated across five harm categories (privacy, non-violent crime, violent crime, intellectual property and jailbreak robustness), using both LLM-as-a-judge and human annotation. The exercise shows how safety behaviours can vary across languages. These include differences in safeguard robustness across languages and harm types and variation in evaluator reliability (LLM-as-judge vs. human review). Further, it also generated methodological insights for improving multilingual safety evaluations, such as the need for culturally contextualised translations, stress-tested evaluator prompts and clearer human annotation guidelines. This work represents an initial step toward a shared framework for multilingual safety testing of advanced AI systems and calls for continued collaboration with the wider research community and industry.

Ee Wei Seah, Yongsen Zheng, Naga Nikshith et al.

The rapid rise of autonomous AI systems and advancements in agent capabilities are introducing new risks due to reduced oversight of real-world interactions. Yet agent testing remains nascent and is still a developing science. As AI agents begin to be deployed globally, it is important that they handle different languages and cultures accurately and securely. To address this, participants from The International Network for Advanced AI Measurement, Evaluation and Science, including representatives from Singapore, Japan, Australia, Canada, the European Commission, France, Kenya, South Korea, and the United Kingdom have come together to align approaches to agentic evaluations. This is the third exercise, building on insights from two earlier joint testing exercises conducted by the Network in November 2024 and February 2025. The objective is to further refine best practices for testing advanced AI systems. The exercise was split into two strands: (1) common risks, including leakage of sensitive information and fraud, led by Singapore AISI; and (2) cybersecurity, led by UK AISI. A mix of open and closed-weight models were evaluated against tasks from various public agentic benchmarks. Given the nascency of agentic testing, our primary focus was on understanding methodological issues in conducting such tests, rather than examining test results or model capabilities. This collaboration marks an important step forward as participants work together to advance the science of agentic evaluations.

Gathoni Ireri, Cecil Abungu, Jean Cheptumo et al.

Frontier AI systems are being adopted across Africa, yet most AI safety evaluations are designed and validated in Western environments. In this paper, we argue that the portability gap can leave Africa-centric pathways to severe harm untested when frontier AI systems are embedded in materially constrained and interdependent infrastructures. We define severe AI risks as material risks from frontier AI systems that result in critical harm, measured as the grave injury or death of thousands of people or economic loss and damage equivalent to five percent of a country's GDP. To support AI safety evaluation design, we develop a taxonomy for identifying Africa-centric severe AI risks. The taxonomy links outcome thresholds to process pathways that model risk as the intersection of hazard, vulnerability, and exposure. We distinguish severe risks by amplification and suddenness, where amplification requires that frontier AI be a necessary magnifier of latent danger and suddenness captures harms that materialise rapidly enough to overwhelm ordinary coping and governance capacity. We then propose threat modelling strategies for African contexts, surveying reference class forecasting, structured expert elicitation, scenario planning, and system theoretic process analysis, and tailoring them to constraints of limited resources, poor connectivity, limited technical expertise, weak state capacity, and conflict. We also examine AI misalignment risk, concluding that Africa is more likely to expose universal failure modes through distributional shift than to generate distinct pathways of misalignment. Finally, we offer practical guidance for running evaluations under resource constraints, emphasising open and extensible tooling, tiered evaluation pipelines, and sharing methods and findings to broaden evaluation scope.