We consider an adversary looking to steal or attack a black-box machine translation (MT) system , either for financial gain or to exploit model errors. We first show that black-box MT systems can be stolen by querying them with monolingual sentences and training models to imitate their outputs. Using simulated experiments, we demonstrate that MT model stealing is possible even when imitation models have different input data or architectures than their victims . Applying these ideas, we train imitation models that reach within 0.6 BLEU of three production MT systems on both high-resource and low-resource language pairs. We then leverage the similarity of our imitation models to transfer adversarial examples to the production systems. We use gradient-based attacks that expose inputs which lead to semantically-incorrect translations, dropped content, and vulgar model outputs. To mitigate these vulnerabilities, we propose a defense that modifies translation outputs in order to misdirect the optimization of imitation models. This defense degrades imitation model BLEU and attack transfer rates at some cost in  BLEU and inference speed.
Adversaries may look to steal or attack black-box NLP systems , either for financial gain or to exploit model errors. One setting of particular interest is machine translation (MT), where models have high commercial value and errors can be costly. We investigate possible exploitations of black-box MT systems and explore a preliminary defense against such threats. We first show that MT systems can be stolen by querying them with monolingual sentences and training models to imitate their outputs. Using simulated experiments, we demonstrate that MT model stealing is possible even when imitation models have different input data or architectures than their target models . Applying these ideas, we train imitation models that reach within 0.6 BLEU of three production MT systems on both high-resource and low-resource language pairs. We then leverage the similarity of our imitation models to transfer adversarial examples to the production systems. We use gradient-based attacks that expose inputs which lead to semantically-incorrect translations, dropped content, and vulgar model outputs. To mitigate these vulnerabilities, we propose a defense that modifies translation outputs in order to misdirect the optimization of imitation models. This defense degrades the adversary's BLEU score and attack success rate at some cost in the defender's BLEU and inference speed.
R
We consider an adversary looking
Adversaries may look
D
a
R
machine translation (MT) system
NLP systems
R
We
One setting of particular interest is machine translation (MT), where models have high commercial value and errors can be costly. We investigate possible exploitations of black-box MT systems and explore a preliminary defense against such threats. We
D
black-box
R
victims
target models
R
imitation model BLEU and attack transfer rates
the adversary's BLEU score and attack success rate
A
the defender's