The distributed inference framework comprises a group of spatially distributed nodes that acquire observations about a POI and transmit computed summary statistics to the fusion center. Based on the messages received from the nodes, the FC makes a global inference about the POI. The distributed and broadcast nature of such systems makes them quite vulnerable to different types of attacks. This article focuses on efficient mitigation schemes to mitigate the impact of eavesdropping on distributed inference and surveys the currently available approaches along with avenues for future research.