AI 中文总结
本报告测试前沿LLM智能体利用生物工具重新设计肽和蛋白质以规避核酸合成筛查的能力，评估其潜在生物安全风险，为风险与能力评估提供基础。
AI 中文摘要
本报告是先前工作的延续，旨在测试大型语言模型（LLM）驱动的人工智能（AI）智能体与AI赋能生物工具（BTs）交互的能力。尽管近年来生物工具的快速发展为加速科学发现带来了希望，但也引发了关于潜在滥用的重大生物安全担忧。生物安全界特别关注LLM在多大程度上能够降低技术门槛，并协助非专家用户访问和操作生物工具。尽管有此关注，但很少有评估聚焦于在明确威胁模型背景下LLM与生物工具的交互。为弥补这一空白，本报告描述了一项测试，评估前沿LLM驱动的AI智能体利用生物工具重新设计肽和蛋白质以规避核酸合成筛查措施的能力。该任务与生物风险高度相关，评估了AI智能体的一种潜在能力，这种能力可能使关键早期防御层被突破，而该防御层旨在防止多种生物滥用场景。本文呈现的研究结果旨在为生物安全研究人员和AI开发者随着这些技术的发展进行或进一步开展风险与能力评估提供基础。
英文摘要
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.
Comments48 pages, 10 figures
