Contemporary AI foundation models increase biological weapons risk
Roger Brent, T. Greg McKelvey Jr

TL;DR
This paper argues that contemporary AI foundation models significantly increase biological weapons risks by enabling nonexperts to perform complex tasks, challenging previous safety assessments and highlighting the need for better evaluation methods.
Contribution
It demonstrates that advanced AI models can guide nonexperts in biological tasks, undermining assumptions that such development requires tacit knowledge and exposing safety assessment flaws.
Findings
AI models can guide recovery of poliovirus from synthetic DNA
Current models can assist nonexperts in complex biological tasks
Safety assessments underestimate biosecurity risks of AI models
Abstract
The rapid advancement of artificial intelligence has raised concerns about its potential to facilitate biological weapons development. We argue existing safety assessments of contemporary foundation AI models underestimate this risk, largely due to flawed assumptions and inadequate evaluation methods. First, assessments mistakenly assume biological weapons development requires tacit knowledge, or skills gained through hands-on experience that cannot be easily verbalized. Second, they rely on imperfect benchmarks that overlook how AI can uplift both nonexperts and already-skilled individuals. To challenge the tacit knowledge assumption, we examine cases where individuals without formal expertise, including a 2011 Norwegian ultranationalist who synthesized explosives, successfully carried out complex technical tasks. We also review efforts to document pathogen construction processes,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCell Image Analysis Techniques · Genetics, Bioinformatics, and Biomedical Research
MethodsLLaMA
