  Neural machine translation systems are known to be vulnerable to adversarial
test inputs, however, as we show in this paper, these systems are also
vulnerable to training attacks. Specifically, we propose a poisoning attack in
which a malicious adversary inserts a small poisoned sample of monolingual text
into the training set of a system trained using back-translation. This sample
is designed to induce a specific, targeted translation behaviour, such as
peddling misinformation. We present two methods for crafting poisoned examples,
and show that only a tiny handful of instances, amounting to only 0.02% of the
training set, is sufficient to enact a successful attack. We outline a defence
method against said attacks, which partly ameliorates the problem. However, we
stress that this is a blind-spot in modern NMT, demanding immediate attention.
