The next biothreat might start as a line of code. A new class of artificial intelligence (AI) systems—known as protein language models (PLMs)—can design novel proteins with the potential to be used as bioweapons at astonishing speed. Originally developed to accelerate drug discovery, these systems can also propose mutations that make viruses more infectious, harder to detect, and resistant to treatment.
The Trump administration’s newly released AI Action Plan includes a biosecurity section focused on access controls and nucleic acid synthesis screening. That emphasis is understandable but insufficient. Screening systems rely on matching against known pathogens, yet the threat from PLMs is precisely that they generate unknown proteins. Synthesis providers can’t evaluate whether a novel gene encodes a harmful function. And the growing availability of benchtop synthesis machines allows well-resourced actors to bypass providers entirely. In short, the danger has shifted upstream, to the model output itself. At first glance, PLMs resemble large language models (LLMs) such as ChatGPT: Both generate sequences using the same structure—words for LLMs, amino acids for PLMs. But that’s where the similarity ends. Harmful text from LLMs can be detected with filters and keywords. Dangerous proteins cannot be. Whether a protein is safe or harmful depends on complex biological properties—how it folds, what it interacts with, and how it behaves in the body—none of which can be reliably predicted from sequence alone. Today, scientists still need to test these proteins in the lab, using real human biological material, to understand their effects.
The U.S. Cannot Prevent Every AI Biothreat—But It Can Outpace Them | Lawfare
