  The advent of clinical language models integrated into electronic health
records (EHR) for clinical decision support has marked a significant
advancement, leveraging the depth of clinical notes for improved
decision-making. Despite their success, the potential vulnerabilities of these
models remain largely unexplored. This paper delves into the realm of backdoor
attacks on clinical language models, introducing an innovative attention-based
backdoor attack method, BadCLM (Bad Clinical Language Models). This technique
clandestinely embeds a backdoor within the models, causing them to produce
incorrect predictions when a pre-defined trigger is present in inputs, while
functioning accurately otherwise. We demonstrate the efficacy of BadCLM through
an in-hospital mortality prediction task with MIMIC III dataset, showcasing its
potential to compromise model integrity. Our findings illuminate a significant
security risk in clinical decision support systems and pave the way for future
endeavors in fortifying clinical language models against such vulnerabilities.
