Biothreat Benchmark Generation Framework for Evaluating Frontier AI Models III: Implementing the Bacterial Biothreat Benchmark (B3) Dataset
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909951682674688 |
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| author | Ackerman, Gary Wilson, Theodore Kallenborn, Zachary Shoemaker, Olivia Wetzel, Anna Peterson, Hayley Danfora, Abigail LaTourette, Jenna Behlendorf, Brandon Clifford, Douglas |
| author_facet | Ackerman, Gary Wilson, Theodore Kallenborn, Zachary Shoemaker, Olivia Wetzel, Anna Peterson, Hayley Danfora, Abigail LaTourette, Jenna Behlendorf, Brandon Clifford, Douglas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08459 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Biothreat Benchmark Generation Framework for Evaluating Frontier AI Models III: Implementing the Bacterial Biothreat Benchmark (B3) Dataset Ackerman, Gary Wilson, Theodore Kallenborn, Zachary Shoemaker, Olivia Wetzel, Anna Peterson, Hayley Danfora, Abigail LaTourette, Jenna Behlendorf, Brandon Clifford, Douglas Machine Learning Artificial Intelligence Computers and Society Emerging Technologies 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. |
| title | Biothreat Benchmark Generation Framework for Evaluating Frontier AI Models III: Implementing the Bacterial Biothreat Benchmark (B3) Dataset |
| topic | Machine Learning Artificial Intelligence Computers and Society Emerging Technologies |
| url | https://arxiv.org/abs/2512.08459 |