Large language models (LLMs) have achieved remarkable performance on a wide range of tasks. However, recent studies have shown that LLMs can memorize training data and simple repeated tokens can trick the model to leak the data. In this paper, we take a step further and show that certain special characters or their combinations with English letters are stronger memory triggers, leading to more severe data leakage. The intuition is that, since LLMs are trained with massive data that contains a substantial amount of special characters (e.g. structural symbols {, } of JSON files, and @, # in emails and online posts), the model may memorize the co-occurrence between these special characters and the raw texts. This motivates us to propose a simple but effective Special Characters Attack (SCA) to induce training data leakage. Our experiments verify the high effectiveness of SCA against state-of-the-art LLMs: they can leak diverse training data, such as code corpus, web pages, and personally identifiable information, and sometimes generate non-stop outputs as a byproduct. We further show that the composition of the training data corpus can be revealed by inspecting the leaked data -- one crucial piece of information for pre-training high-performance LLMs. Our work can help understand the sensitivity of LLMs to special characters and identify potential areas for improvement.

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The gold-rush has started, and this time, it's not gold that's driving the frenzy -- it's AI. The world is currently witnessing an AI revolution that is changing the way we live, work, and interact. With advancements in machine learning, natural language processing, and computer vision, AI is becoming more intelligent and sophisticated, enabling it to take on complex tasks and decision-making processes. As AI technology continues to evolve, we also need to evolve in order to harness its full potential, avoid being obsolete and mitigate potential risks. ChatGPT is an AI language model developed by OpenAI that can generate human-like responses to text-based prompts.