Can Large Language Models Design Biological Weapons? Evaluating Moremi Bio
Gertrude Hattoh, Jeremiah Ayensu, Nyarko Prince Ofori, Solomon Eshun, Darlington Akogo

TL;DR
This study demonstrates that large language models can be exploited to design highly toxic biological agents, highlighting significant biosecurity risks and the urgent need for mitigation strategies.
Contribution
It provides the first comprehensive assessment of LLM-enabled biodesign risks, showing how these models can generate toxic proteins and molecules, and proposes mitigation approaches.
Findings
Generated 1020 toxic proteins matching known toxins
Produced 5,000 toxic small molecules with high toxicity scores
Identified dual-use risks and proposed mitigation strategies
Abstract
Advances in AI, particularly LLMs, have dramatically shortened drug discovery cycles by up to 40% and improved molecular target identification. However, these innovations also raise dual-use concerns by enabling the design of toxic compounds. Prompting Moremi Bio Agent without the safety guardrails to specifically design novel toxic substances, our study generated 1020 novel toxic proteins and 5,000 toxic small molecules. In-depth computational toxicity assessments revealed that all the proteins scored high in toxicity, with several closely matching known toxins such as ricin, diphtheria toxin, and disintegrin-based snake venom proteins. Some of these novel agents showed similarities with other several known toxic agents including disintegrin eristostatin, metalloproteinase, disintegrin triflavin, snake venom metalloproteinase, corynebacterium ulcerans toxin. Through quantitative risk…
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