  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.
