  The rapid development of large language models (LLMs) has yielded impressive
success in various downstream tasks. However, the vast potential and remarkable
capabilities of LLMs also raise new security and privacy concerns if they are
exploited for nefarious purposes due to their open-endedness. For example, LLMs
may be used to plagiarize or imitate writing, thereby infringing the copyright
of the original content, or to create indiscriminate fake information based on
a certain source text. In some cases, LLMs can even analyze text from the
Internet to infer personal privacy. Unfortunately, previous text protection
research could not foresee the emergence of powerful LLMs, rendering it no
longer effective in this new context. To bridge this gap, we introduce Silent
Guardian (SG), a text protection mechanism against LLMs, which allows LLMs to
refuse to generate response when receiving protected text, preventing the
malicious use of text from the source. Specifically, we first propose the
concept of Truncation Protection Examples (TPE). By carefully modifying the
text to be protected, TPE can induce LLMs to first sample the end token, thus
directly terminating the interaction. In addition, to efficiently construct TPE
in the discrete space of text data, we propose a novel optimization algorithm
called Super Tailored Protection (STP), which is not only highly efficient but
also maintains the semantic consistency of the text during the optimization
process. The comprehensive experimental evaluation demonstrates that SG can
effectively protect the target text under various configurations and achieve
almost 100% protection success rate in some cases. Notably, SG also exhibits
relatively good transferability and robustness, making its application in
practical scenarios possible.
