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. BDTs have the potential to improve vaccine design and drug discovery, but may also be misused deliberately or inadvertently to design biological agents capable of doing more harm or evading current 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 a decentralized, non-commercial, open-source manner. We propose a range of measures to mitigate misuse risks. These include measures to control model development, assess risks, encourage transparency, manage access to dangerous capabilities, and strengthen cybersecurity. Implementing such measures will require close coordination between developers and governments.