大型语言模型在提示注入攻击下的机器翻译扩展行为
Scaling Behavior of Machine Translation with Large Language Models under Prompt Injection Attacks
AI总结：
本研究首次在多语言机器翻译任务中，系统探究提示注入攻击下模型规模与攻击成功率的关系，发现大模型更易受攻击的逆缩放现象，并引入新基准数据集。
摘要：
大型语言模型（LLMs）正日益成为许多自然语言处理任务（如机器翻译）的首选基础平台，这得益于其质量通常可与任务专用模型相媲美甚至更优，以及通过自然语言指令或上下文示例指定任务的简便性。然而，它们的通用性也使其容易受到最终用户的破坏，用户可能在请求中嵌入指令，导致模型以未经授权且可能不安全的方式行事。在本工作中，我们研究了多种LLM家族在机器翻译任务上的提示注入攻击（PIAs），重点关注模型规模对攻击成功率的影响。我们引入了一个新的基准数据集，并发现对于多种语言对和以英语编写的注入提示，在某些条件下，较大的模型可能更容易受到成功攻击，这是逆缩放现象的一个实例（McKenzie等人，2023）。据我们所知，这是首个在多语言环境中研究非平凡LLM扩展行为的工作。
英文摘要：
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.
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