As advancements in artificial intelligence propel progress in the life sciences, they may also enable the weaponisation and misuse of biological agents. This article differentiates two classes of AI tools that pose such biosecurity risks: large language models (LLMs) and biological design tools (BDTs). LLMs, such as GPT-4, are already able to provide dual-use information that could have enabled historical biological weapons efforts to succeed. As LLMs are turned into lab assistants and autonomous science tools, this will further increase their ability to support research. Thus, LLMs will in particular lower barriers to biological misuse. In contrast, BDTs will expand the capabilities of sophisticated actors. Concretely, BDTs may enable the creation of pandemic pathogens substantially worse than anything seen to date and could enable forms of more predictable and targeted biological weapons. In combination, LLMs and BDTs could raise the ceiling of harm from biological agents and could make them broadly accessible. The differing risk profiles of LLMs and BDTs have important implications for risk mitigation. LLM risks require urgent action and might be effectively mitigated by controlling access to dangerous capabilities. Mandatory pre-release evaluations could be critical to ensure that developers eliminate dangerous capabilities. Science-specific AI tools demand differentiated strategies to allow access to legitimate users while preventing misuse. Meanwhile, risks from BDTs are less defined and require monitoring by developers and policymakers. Key to reducing these risks will be enhanced screening of gene synthesis, interventions to deter biological misuse by sophisticated actors, and exploration of specific controls of BDTs.

翻译：随着人工智能的进步推动生命科学发展，它也可能导致生物制剂的武器化及滥用风险。本文区分了构成此类生物安全风险的两类AI工具：大型语言模型（LLMs）与生物设计工具（BDTs）。LLMs（如GPT-4）已能提供双用途信息，可能助长历史上生物武器研发尝试的成功。当LLMs被转化为实验室助手和自主科研工具时，其支持研究的能力将进一步增强。因此，LLMs将特别降低生物滥用的门槛。相比之下，BDTs将扩展高技术水平行为者的能力。具体而言，BDTs可能催生比迄今所见更为严重的疫情病原体，并生成更可预测、更具靶向性的生物武器。二者结合可能提升生物制剂危害的上限，并使其广泛可及。LLMs与BDTs的不同风险特征对风险缓解具有重要启示：LLM风险需紧急应对，通过控制危险能力接入可有效缓解；强制预发布评估对确保开发者消除危险能力至关重要。科学专用AI工具需要差异化策略，在允许合法用户访问的同时防止滥用。与此同时，BDTs的风险尚未明确，需要开发者和政策制定者持续监测。降低此类风险的关键在于加强基因合成筛查、干预高技术水平行为者的生物滥用行为，并探索对BDTs的特定管控措施。