Backdoor Attack on Multilingual Machine Translation
Jun Wang Qiongkai Xu Xuanli He Benjamin I. P. Rubinstein Trevor Cohn
While multilingual machine translation (MNMT) systems hold substantial
promise, they also have security vulnerabilities. Our research highlights that
MNMT systems can be susceptible to a particularly devious style of backdoor
attack, whereby an attacker injects poisoned data into a low-resource language
pair to cause malicious translations in other languages, including
high-resource languages. Our experimental results reveal that injecting less
than 0.01% poisoned data into a low-resource language pair can achieve an
average 20% attack success rate in attacking high-resource language pairs. This
type of attack is of particular concern, given the larger attack surface of
languages inherent to low-resource settings. Our aim is to bring attention to
these vulnerabilities within MNMT systems with the hope of encouraging the
community to address security concerns in machine translation, especially in
the context of low-resource languages.