Medical Foundation Models are Susceptible to Targeted Misinformation
  Attacks
Tianyu Han Sven Nebelung Firas Khader Tianci Wang Gustav Mueller-Franzes Christiane Kuhl Sebastian Försch Jens Kleesiek Christoph Haarburger Keno K. Bressem Jakob Nikolas Kather Daniel Truhn
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 model's weights, we can deliberately inject an incorrect
biomedical fact. The erroneous information is then propagated in the model's
output, whilst its performance on other biomedical tasks remains intact. We
validate our findings in a set of 1,038 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.