AI models can modify the genetic sequences of known bioweapons like ricin just enough to evade current screening protocols at DNA synthesis companies. This creates functional but 'obfuscated' threats, demonstrating a critical vulnerability in our biodefense supply chain.

Models designed to predict and screen out compounds toxic to human cells have a serious dual-use problem. A malicious actor could repurpose the exact same technology to search for or design novel, highly toxic molecules for which no countermeasures exist, a risk the researchers initially overlooked.

Top AI labs and biotech firms are urging the US government to mandate screening for nucleic acid synthesis orders. This pragmatic approach targets a concrete threat—AI-assisted bioweapon creation—rather than abstract superintelligence risks.

The current security landscape presents a paradox. While AI creates a new, complex threat surface, it also provides defenders with unprecedented tools. For example, building a software taxonomy, a task that once took years and hundreds of researchers, can now be done in weeks using AI agents.

The danger of AI creating harmful proteins is not in the digital design but in its physical creation. A protein sequence on a computer is harmless. The critical control point is the gene synthesis process. Therefore, biosecurity efforts should focus on providing advanced screening tools to synthesis providers.

Current concerns focus on AI agents using existing bioinformatics tools. The more advanced threat is agentic AI that can code and create novel, personalized biological tools on demand, moving beyond a static toolset to a dynamic threat generation capability.

Current biosecurity screens for threats by matching DNA sequences to known pathogens. However, AI can design novel proteins that perform a harmful function without any sequence similarity to existing threats. This necessitates new security tools that can predict a protein's function, a concept termed "defensive acceleration."

Existing bio-defense systems work by matching DNA sequences to known pathogen databases. However, generative AI can create novel sequences with different 'spellings' but the same dangerous function. Effective future defense must evolve to predict a sequence's function, not just its identity.

"Gain-of-function" research, intended to develop countermeasures for dangerous pathogens, uses the same methods as offensive bioweapon creation. This paradox means that efforts to defend against biological threats inherently increase the risk of creating them, whether deliberately or by accident.