We propose a tractable one-tick model of the limit order book to characterize when an algorithm will learn to spoof the order book if decision making is delegated to a learning algorithm. We obtain simple testable conditions that depend only on the model parameters and are easy to estimate. Applying the testable conditions to empirical data, we find that market conditions from Nasdaq are conducive for an algorithm to learn to spoof the order book.