  We study data poisoning attacks in the online setting where training items
arrive sequentially, and the attacker may perturb the current item to
manipulate online learning. Importantly, the attacker has no knowledge of
future training items nor the data generating distribution. We formulate online
data poisoning attack as a stochastic optimal control problem, and solve it
with model predictive control and deep reinforcement learning. We also upper
bound the suboptimality suffered by the attacker for not knowing the data
generating distribution. Experiments validate our control approach in
generating near-optimal attacks on both supervised and unsupervised learning
tasks.
