  Glauber dynamics are a natural model of dynamics of dependent systems. While
originally introduced in statistical physics, they have found important
applications in the study of social networks, computer vision and other
domains. In this work, we introduce a model of corrupted Glauber dynamics
whereby instead of updating according to the prescribed conditional
probabilities, some of the vertices and their updates are controlled by an
adversary. We study the effect of such corruptions on global features of the
system. Among the questions we study are: How many nodes need to be controlled
in order to change the average statistics of the system in polynomial time? And
how many nodes are needed to obstruct approximate convergence of the dynamics?
  Our results can be viewed as studying the robustness of classical sampling
methods and are thus related to robust inference. The proofs connect to
classical theory of Glauber dynamics from statistical physics.
