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.

翻译：大语言模型（LLMs）正日益成为机器翻译等许多自然语言处理任务的首选基础平台，其优势在于质量常能与任务专用模型媲美甚至更优，且可通过自然语言指令或上下文示例简单指定任务。然而，这种通用性使其易受终端用户破坏——用户可能在其请求中嵌入指令，导致模型作出未经授权且可能不安全的响应。本研究针对机器翻译任务，考察多个大语言模型家族中的提示注入攻击（PIAs），重点分析模型规模对攻击成功率的影响。我们引入一个新的基准数据集，发现在多种语言对和英语注入提示条件下，较大模型在特定情况下可能更易遭受成功攻击，这体现了逆缩放现象（McKenzie等人，2023年）。据我们所知，这是首个在多语言环境中研究非平凡大语言模型缩放行为的工作。