The Operational Risks of AI in Large-Scale Biological Attacks
A Red-Team Approach
Bibliographic Data
| ID | 19910727 |
|---|---|
| Authors | Christopher Mouton (0000-0002-1449-2187), Caleb Lucas (0000-0003-0049-4680), Ella Guest (0000-0001-8940-2871) |
| Year | 2023 |
| Publication date | 2023-01-01 |
| Open Access | No |
| Type | BOOK |
| Publisher | RAND Corporation (PUBLISHER • US) |
| DOI | 10.7249/rra2977-1 |
| OpenAlex | W4387681289 |
| Language | EN |
| Citations received | 3 |
| References cited | 1 |
In this report, the authors address the emerging issue of identifying and mitigating the risks posed by the misuse of artificial intelligence (AI)—specifically, large language models—in the context of biological attacks and present preliminary findings of their research. They find that while AI can generate concerning text, the operational impact is a subject for future research
Archaeology · Cartography · Context (archaeology) · Data science · Geography · Scale (ratio) · Subject (documents) · World Wide Web · Artificial Intelligence · Computer Science · Misinformation and Its Impacts
| Citations per year | 1.5 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |