  Recent advancements in generative machine learning have enabled rapid
progress in biological design tools (BDTs) such as protein structure and
sequence prediction models. The unprecedented predictive accuracy and novel
design capabilities of BDTs present new and significant dual-use risks. For
example, their predictive accuracy allows biological agents, whether vaccines
or pathogens, to be developed more quickly, while the design capabilities could
be used to discover drugs or evade DNA screening techniques. Similar to other
dual-use AI systems, BDTs present a wicked problem: how can regulators uphold
public safety without stifling innovation? We highlight how current regulatory
proposals that are primarily tailored toward large language models may be less
effective for BDTs, which require fewer computational resources to train and
are often developed in an open-source manner. We propose a range of measures to
mitigate the risk that BDTs are misused, across the areas of responsible
development, risk assessment, transparency, access management, cybersecurity,
and investing in resilience. Implementing such measures will require close
coordination between developers and governments.
