Researchers at ETH Zurich have formalized models of the EMV payment protocol using the Tamarin model checker. They discovered flaws allowing attackers to bypass PIN requirements for high-value purchases on EMV cards like Mastercard and Visa. The team also collaborated with an EMV consortium member to verify the improved EMV Kernel C-8 protocol. Why it matters: This research highlights the importance of formal methods in identifying critical vulnerabilities in widely used payment systems, potentially impacting financial security for consumers in the GCC region and worldwide.
A new methodology emulating fact-checker criteria assesses news outlet factuality and bias using LLMs. The approach uses prompts based on fact-checking criteria to elicit and aggregate LLM responses for predictions. Experiments demonstrate improvements over baselines, with error analysis on media popularity and region, and a released dataset/code at https://github.com/mbzuai-nlp/llm-media-profiling.
Dr. Xinwei Sun from Microsoft Research Asia presented research on trustworthy AI, focusing on statistical learning with theoretical guarantees. The work covers methods for sparse recovery with false-discovery rate analysis and causal inference tools for robustness and explainability. Consistency and identifiability were addressed theoretically, with applications shown in medical imaging analysis. Why it matters: The research contributes to addressing key limitations of current AI models regarding explainability, reproducibility, robustness, and fairness, which are crucial for real-world applications in sensitive fields like healthcare.
A report published by Microsoft indicates a 1.5% increase in global AI adoption during the first quarter of 2026. This data was released through CoinGeek, suggesting a broad analysis of worldwide trends in artificial intelligence integration. The report likely offers insights into how various industries are incorporating AI technologies across different regions. Why it matters: Such global reports provide a benchmark for understanding broader AI development, though specific regional impacts for the Middle East would require deeper analysis of the full report content.
KAUST's Urban Lab is developing the Saudi National Life Cycle Inventory, an environmental database providing quantitative data on the environmental impact of products and processes in Saudi Arabia. The database includes information on raw material use, energy consumption, water usage, waste generation, and air pollutants specific to the Kingdom. This project was highlighted at the 'Greening the Giga' workshop, where KAUST also released a report on building a national framework for Life Cycle Assessments. Why it matters: The database and framework can guide multiple sectors in adopting green technology and help Saudi Arabia achieve its net-zero carbon emissions goal by 2060.