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Can large language models democratize access to dual-use biotechnology?

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arxiv 2306.03809 v1 pith:XHJ4HIRA submitted 2023-06-06 cs.CY cs.AI

classification cs.CYcs.AI
keywords chatbotsllmsmodelsresearchaccesscontractdual-usegenerated
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) such as those embedded in 'chatbots' are accelerating and democratizing research by providing comprehensible information and expertise from many different fields. However, these models may also confer easy access to dual-use technologies capable of inflicting great harm. To evaluate this risk, the 'Safeguarding the Future' course at MIT tasked non-scientist students with investigating whether LLM chatbots could be prompted to assist non-experts in causing a pandemic. In one hour, the chatbots suggested four potential pandemic pathogens, explained how they can be generated from synthetic DNA using reverse genetics, supplied the names of DNA synthesis companies unlikely to screen orders, identified detailed protocols and how to troubleshoot them, and recommended that anyone lacking the skills to perform reverse genetics engage a core facility or contract research organization. Collectively, these results suggest that LLMs will make pandemic-class agents widely accessible as soon as they are credibly identified, even to people with little or no laboratory training. Promising nonproliferation measures include pre-release evaluations of LLMs by third parties, curating training datasets to remove harmful concepts, and verifiably screening all DNA generated by synthesis providers or used by contract research organizations and robotic cloud laboratories to engineer organisms or viruses.

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Cited by 3 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. A Survey on Training-free Alignment of Large Language Models

    cs.CL 2025-08 conditional novelty 4.0 of 10

    A survey that catalogs and categorizes training-free LLM alignment methods into pre-decoding, in-decoding, and post-decoding, with a limited experimental comparison on one model.

  3. 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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