  A common assumption in the social learning literature is that agents exchange
information in an unselfish manner. In this work, we consider the scenario
where a subset of agents aims at driving the network beliefs to the wrong
hypothesis. The adversaries are unaware of the true hypothesis. However, they
will "blend in" by behaving similarly to the other agents and will manipulate
the likelihood functions used in the belief update process to launch
inferential attacks. We will characterize the conditions under which the
network is misled. Then, we will explain that it is possible for such attacks
to succeed by showing that strategies exist that can be adopted by the
malicious agents for this purpose. We examine both situations in which the
agents have minimal or no information about the network model.
