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的发展，它也可能助长生物制剂的武器化与误用。本文区分了两类具有此类生物安全风险的人工智能工具：大型语言模型（LLMs）与生物设计工具（BDTs）。诸如GPT-4等LLMs已能提供双重用途信息，这些信息可能助推历史上生物武器研制企图得逞。随着LLMs逐渐转变为实验室助手和自主科学工具，其支持研究的能力将进一步增强。因此，LLMs将尤其降低生物误用的门槛。相比之下，BDTs将扩展高水平行为者的能力。具体而言，BDTs可能促成远超现有任何病原体的疫情级病原体产生，并可能催生更具可预测性和靶向性的生物武器形式。两者结合，LLMs与BDTs可能提高生物制剂危害的上限，并使其广泛可得。LLMs与BDTs不同的风险特征对风险缓解具有重要意义：LLM风险需紧急应对，通过控制危险能力的获取可有效缓解，强制性的发布前评估对于确保开发者消除危险能力至关重要；面向科学的AI工具需采取差异化策略，在允许合法用户访问的同时防止误用。与此同时，BDT风险尚未明确，需由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、遏制高水平行为者生物误用的干预措施，以及探索针对BDT的特定管控手段。