  Spam filters are a crucial component of modern email systems, as they help to
protect users from unwanted and potentially harmful emails. However, the
effectiveness of these filters is dependent on the quality of the machine
learning models that power them. In this paper, we design backdoor attacks in
the domain of spam filtering. By demonstrating the potential vulnerabilities in
the machine learning model supply chain, we highlight the need for careful
consideration and evaluation of the models used in spam filters. Our results
show that the backdoor attacks can be effectively used to identify
vulnerabilities in spam filters and suggest the need for ongoing monitoring and
improvement in this area.
