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This paper presents the pilot implementation of the Bacterial Biothreat Benchmark (B3) dataset, designed to evaluate the biosecurity risks associated with frontier AI models. The B3 dataset was used to probe a sample frontier AI model, and the model's responses were then evaluated by humans, followed by risk analysis. The pilot study demonstrated the B3 dataset's utility in rapidly assessing biosecurity risks, pinpointing their origins, and guiding mitigation efforts.
A new benchmark dataset, B3, offers a practical way to quantify and mitigate the biosecurity risks of LLMs, revealing specific vulnerabilities and guiding targeted interventions.
The potential for rapidly-evolving frontier artificial intelligence (AI) models, especially large language models (LLMs), to facilitate bioterrorism or access to biological weapons has generated significant policy, academic, and public concern. Both model developers and policymakers seek to quantify and mitigate any risk, with an important element of such efforts being the development of model benchmarks that can assess the biosecurity risk posed by a particular model. This paper discusses the pilot implementation of the Bacterial Biothreat Benchmark (B3) dataset. It is the third in a series of three papers describing an overall Biothreat Benchmark Generation (BBG) framework, with previous papers detailing the development of the B3 dataset. The pilot involved running the benchmarks through a sample frontier AI model, followed by human evaluation of model responses, and an applied risk analysis of the results along several dimensions. Overall, the pilot demonstrated that the B3 dataset offers a viable, nuanced method for rapidly assessing the biosecurity risk posed by a LLM, identifying the key sources of that risk and providing guidance for priority areas of mitigation priority.