  We extend the model of stochastic bandits with adversarial corruption
(Lykouriset al., 2018) to the stochastic linear optimization problem (Dani et
al., 2008). Our algorithm is agnostic to the amount of corruption chosen by the
adaptive adversary. The regret of the algorithm only increases linearly in the
amount of corruption. Our algorithm involves using L\"owner-John's ellipsoid
for exploration and dividing time horizon into epochs with exponentially
increasing size to limit the influence of corruption.
