[2606.11150] ABC-Bench: An Agentic Bio-Capabilities Benchmark for Biosecurity

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Computer Science > Artificial Intelligence

arXiv:2606.11150 (cs)

[Submitted on 9 Jun 2026]

Title:ABC-Bench: An Agentic Bio-Capabilities Benchmark for Biosecurity

Authors:Andrew Bo Liu, Samira Nedungadi, Bryce Cai, Alex Kleinman, Harmon Bhasin, Seth Donoughe<br>View a PDF of the paper titled ABC-Bench: An Agentic Bio-Capabilities Benchmark for Biosecurity, by Andrew Bo Liu and 5 other authors

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Abstract:Large language models (LLMs) are rapidly acquiring capabilities relevant to biological research, from literature synthesis to interpretation of experimental data. Increasingly, LLM agents can also perform in silico biology tasks that previously required experienced human biologists. These emerging AI capabilities offer new opportunities for scientific discovery and biomedical advances, but they also shift the landscape of biosecurity risks. To address this, we introduce the Agentic Bio-Capabilities Benchmark (ABC-Bench), a suite of tasks to measure agentic biosecurity-relevant capabilities. ABC-Bench evaluates LLM agents on both benign and dual-use biology tasks: writing code to operate liquid handling robots, designing DNA fragments for in vitro assembly, and evading DNA synthesis screening. These tasks require a combination of biology and software expertise. All tested LLM agents outperformed the median expert human baseliner on all three tasks. Agents performed highly on tasks drawing on published knowledge and well-documented protocols, and more weakly on a task requiring novel bioinformatics reasoning. In three wet-lab validation experiments, we found that OpenAI's o4-mini-high produced scripts that, when run on an OpenTrons liquid handling robot, successfully assembled DNA with expected sequences.

Comments:<br>18 pages. To be published in ICML 2026

Subjects:

Artificial Intelligence (cs.AI); Computers and Society (cs.CY)

Cite as:<br>arXiv:2606.11150 [cs.AI]

(or<br>arXiv:2606.11150v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.11150

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Andrew Liu [view email]<br>[v1]<br>Tue, 9 Jun 2026 17:35:37 UTC (1,581 KB)

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