Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support
Abstract Background Large language models (LLMs) show promise in clinical contexts but can generate false facts (often referred to as “hallucinations”). One subset of these errors arises from adversarial attacks, in which fabricated details embedded in prompts lead the model to produce or elaborate...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-01021-3 |
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