# Resilient Biosecurity in the Era of AI-Enabled Bioweapons

arXiv: https://arxiv.org/abs/2509.02610 · HTML: https://mlsanity.com/paper/2509.02610/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 53 references that are in the map.
- First posted on arXiv 30 Aug 2025.

## Builds on (most cited first, 1 of 1)

- [Protein Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2502.17504/) (2025)

## Built on by (most cited first, 3 of 3)

- [Best Practices for Biorisk Evaluations on Open-Weight Bio-Foundation Models](https://mlsanity.com/paper/2510.27629/) (2025)
- [Know Your Scientist: KYC as Biosecurity Infrastructure](https://mlsanity.com/paper/2602.06172/) (2026)
- [A Shortcut to Structure in AlphaFold 3](https://mlsanity.com/paper/2610.08937/) (2026)

## Abstract (arXiv)

Recent advances in generative biology have enabled the design of novel proteins, creating significant opportunities for drug discovery while also introducing new risks, including the potential development of synthetic bioweapons. Existing biosafety measures primarily rely on inference-time filters such as sequence alignment and protein-protein interaction (PPI) prediction to detect dangerous outputs. In this study, we evaluate the performance of three leading PPI prediction tools: AlphaFold 3, AF3Complex, and SpatialPPIv2. These models were tested on well-characterized viral-host interactions, such as those involving Hepatitis B and SARS-CoV-2. Despite being trained on many of the same viruses, the models fail to detect a substantial number of known interactions. Strikingly, none of the tools successfully identify any of the four experimentally validated SARS-CoV-2 mutants with confirmed binding. These findings suggest that current predictive filters are inadequate for reliably flagging even known biological threats and are even more unlikely to detect novel ones. We argue for a shift toward response-oriented infrastructure, including rapid experimental validation, adaptable biomanufacturing, and regulatory frameworks capable of operating at the speed of AI-driven developments.