Medical large language models are susceptible to targeted misinformation attacks - Han, Tianyu; Nebelung, Sven; Khader, Firas; Wang, Tianci; Müller-Franzes, Gustav; Kuhl, Christiane; Försch, Sebastian; Kleesiek, Jens Philipp; Haarburger, Christoph; Bressem, Keno K.; Kather, Jakob Nikolas; Truhn, Daniel (npj digital medicine, 2024)
Large language models (LLMs) have broad medical knowledge and can reason about medical information across many domains, holding promising potential for diverse medical applications in the near future. In this study, we demonstrate a concerning vulnerability of LLMs in medicine. Through targeted manipulation of just 1.1% of the weights of the LLM, we can deliberately inject incorrect biomedical facts. The erroneous information is then propagated in the model’s output while maintaining performance on other biomedical tasks. We validate our findings in a set of 1025 incorrect biomedical facts. This peculiar susceptibility raises serious security and trustworthiness concerns for the application of LLMs in healthcare settings. It accentuates the need for robust protective measures, thorough verification mechanisms, and stringent management of access to these models, ensuring their reliable and safe use in medical practice.
Gesehen am 23.04.2025