Scaling Behavior of Machine Translation with Large Language Models under
  Prompt Injection Attacks
Zhifan Sun Antonio Valerio Miceli-Barone
Large Language Models (LLMs) are increasingly becoming the preferred
foundation platforms for many Natural Language Processing tasks such as Machine
Translation, owing to their quality often comparable to or better than
task-specific models, and the simplicity of specifying the task through natural
language instructions or in-context examples. Their generality, however, opens
them up to subversion by end users who may embed into their requests
instructions that cause the model to behave in unauthorized and possibly unsafe
ways. In this work we study these Prompt Injection Attacks (PIAs) on multiple
families of LLMs on a Machine Translation task, focusing on the effects of
model size on the attack success rates. We introduce a new benchmark data set
and we discover that on multiple language pairs and injected prompts written in
English, larger models under certain conditions may become more susceptible to
successful attacks, an instance of the Inverse Scaling phenomenon (McKenzie et
al., 2023). To our knowledge, this is the first work to study non-trivial LLM
scaling behaviour in a multi-lingual setting.