The role of AI safety benchmarks in evaluating systemic risks in general-purpose AI models : collection of external scientific studies on general-purpose AI models under the EU AI Act

The evaluation of systemic risks in General-Purpose AI (GPAI) models is a complex challenge that requires a multifaceted approach, extending beyond traditional capability assessments. This report analyses the role of AI safety benchmarks in identifying systemic risks in GPAI models, proposing a dual-trigger framework that combines capability triggers with safety benchmarks to provide a more comprehensive assessment of potential harms. The current landscape of safety benchmarks is still in development, with various initiatives emerging to address specific systemic risk categories, such as the ones identified in the GPAI Code of Practice: cyber offence, chemical, biological, radiological and nuclear (CBRN) risks, harmful manipulation, and loss of control. A tiered evaluation strategy is recommended, applying more rigorous and costly safety evaluations only to models that meet a predefined capability threshold or are intended for deployment in high-risk domains, ensuring proportionality and efficient resource allocation. Ultimately, the development of robust and standardised safety benchmarks is relevant for accurate classification of GPAI models as GPAI models with systemic risk, and policy initiatives should incentivise their creation to enable more effective systemic risk identification.