Explore/benchmark/Testing Large Language Model Agents on the Use of Biological Tools for Nucleic Acid Synthesis Screening Evasion
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Jeffrey Lee, Alyssa Worland, Christopher Rodriguez, Kyle Brady, Grant Ellison, Henry Alexander Bradley, Dawid Maciorowski, Jordan Despanie, Barbara Del Castello, Jason Johnson, Steph Guerra/Testing Large Language Model Agents on the Use of Biological Tools for Nucleic Acid Synthesis Screening EvasionUnknown

This report is a continuation of previous efforts to test the ability of large language model (LLM)-driven artificial intelligence (AI) agents to interface with AI-enabled biological tools (BTs). While rapid advancements in BTs in recent years have brought promise to accelerate scientific discovery, they also raise significant biosecurity concerns about potential misuse. The biosecurity community is particularly interested in the extent to which LLMs can lower technical barriers and assist non-expert users in accessing and operating BTs. Despite this interest, few evaluations have focused on LLM-BT interactions in the context of a defined threat model. To address this gap, this report describes a test of frontier LLM-driven AI Agents on their ability to use BTs to redesign peptides and proteins to evade nucleic acid synthesis screening measures. Highly relevant to biorisk, this task assesses a potential capability of AI agents that could enable a breach of a critical early defensive layer designed to prevent a multitude of biological misuse scenarios. The findings presented here intend to offer a foundation for biosecurity researchers and AI developers to conduct or further risk and capability assessments as these technologies progress.

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