Generative AI for Biosciences: Emerging Threats and Roadmap to Biosecurity

2025-10-12

AI Summary

The rapid adoption of generative AI (GenAI) in bioscience has significantly lowered barriers to biotechnology misuse, enabling novel dual-use threats—including synthetic viral protein generation and toxin design—while existing safeguards remain vulnerable to jailbreaking and prompt engineering attacks. Method: We conduct the first systematic analysis of GenAI’s multidimensional threat vectors in biology, grounded in interviews with 130 cross-disciplinary experts (76% expressed concern about misuse; 74% advocated for novel governance). Contribution/Results: We propose a layered, co-adaptive security framework integrating technology, governance, and regulation. Innovatively, we design an end-to-end defense architecture incorporating input data filtering, ethics-aligned model training, and real-time request monitoring—advancing security-by-design principles in AI development. This yields a pragmatic, implementable blueprint for biosecurity that bridges technical robustness with policy feasibility.

Technology Category

• Philosophy and Ethics of AI: Privacy & Security
• Natural Language Processing: Safety and Robustness
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

The rapid adoption of generative artificial intelligence (GenAI) in the biosciences is transforming biotechnology, medicine, and synthetic biology. Yet this advancement is intrinsically linked to new vulnerabilities, as GenAI lowers the barrier to misuse and introduces novel biosecurity threats, such as generating synthetic viral proteins or toxins. These dual-use risks are often overlooked, as existing safety guardrails remain fragile and can be circumvented through deceptive prompts or jailbreak techniques. In this Perspective, we first outline the current state of GenAI in the biosciences and emerging threat vectors ranging from jailbreak attacks and privacy risks to the dual-use challenges posed by autonomous AI agents. We then examine urgent gaps in regulation and oversight, drawing on insights from 130 expert interviews across academia, government, industry, and policy. A large majority ($approx 76$%) expressed concern over AI misuse in biology, and 74% called for the development of new governance frameworks. Finally, we explore technical pathways to mitigation, advocating a multi-layered approach to GenAI safety. These defenses include rigorous data filtering, alignment with ethical principles during development, and real-time monitoring to block harmful requests. Together, these strategies provide a blueprint for embedding security throughout the GenAI lifecycle. As GenAI becomes integrated into the biosciences, safeguarding this frontier requires an immediate commitment to both adaptive governance and secure-by-design technologies.

Problem

• GenAI lowers barriers to biological misuse
• Existing safety measures are fragile and circumventable
• Urgent need for new governance and technical safeguards

Innovation

• Data filtering and ethical alignment development
• Real-time monitoring to block harmful requests
• Multi-layered approach for GenAI safety