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Artificial intelligence and biological misuse: Differentiating risks of language models and biological design tools

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arxiv 2306.13952 v8 pith:JKTLGQ6B submitted 2023-06-24 cs.CY

classification cs.CY
keywords biologicalllmstoolsbdtsrisksenablemisusemodels
verification ladder T0 review T1 audit T2 compute T3 formal
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As advancements in artificial intelligence (AI) 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 could pose such biosecurity risks: large language models (LLMs) and biological design tools (BDTs). LLMs, such as GPT-4 and its successors, might provide dual-use information and thus remove some barriers encountered by historical biological weapons efforts. As LLMs are turned into multi-modal lab assistants and autonomous science tools, this will increase their ability to support non-experts in performing laboratory work. Thus, LLMs may 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, the convergence of LLMs and BDTs could raise the ceiling of harm from biological agents and could make them broadly accessible. A range of interventions would help to manage risks. Independent pre-release evaluations could help understand the capabilities of models and the effectiveness of safeguards. Options for differentiated access to such tools should be carefully weighed with the benefits of openly releasing systems. Lastly, essential for mitigating risks will be universal and enhanced screening of gene synthesis products.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Early Warning of Emerging Biosecurity Risks in Frontier LLMs

    cs.CL 2026-07 reject novelty 5.0 of 10

    A bio-red-teaming model is reported to jailbreak 14 frontier LLMs into producing dangerous biosecurity outputs, but the claimed wet-lab physical verification was not actually carried out.

  2. Harmonizing AI Safety Thresholds

    cs.AI 2026-07 conditional novelty 5.0 of 10

    The authors propose harmonized AI capability floors: non-zero full-chain TLO cyber completion triggers safeguards, and AI progress at 5× trend for 3 months triggers safeguards, with biorisk left as a diagnostic.

  3. Domestic frontier AI regulation, an IAEA for AI, an NPT for AI, and a US-led Allied Public-Private Partnership for AI: Four institutions for governing and developing frontier AI

    cs.CY 2025-07 accept novelty 5.0 of 10

    Compute governance can underpin four institutions for frontier AI: domestic regulation, an International AI Agency, a Secure Chips Agreement, and a US-led Allied Public-Private Partnership.

  4. Can Large Language Models Design Biological Weapons? Evaluating Moremi Bio

    q-bio.QM 2025-05 reject novelty 2.0 of 10

    A red-team test of Moremi Bio Agent produced thousands of computationally predicted toxic sequences, but without experimental validation or shared data the claim that LLMs can design bioweapons is unsupported.

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