# Silent Guardian: Protecting Text from Malicious Exploitation by Large Language Models

arXiv: https://arxiv.org/abs/2312.09669 · HTML: https://mlsanity.com/paper/2312.09669/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2023.

## Built on by (most cited first, 8 of 8)

- [Safeguarding Large Language Models: A Survey](https://mlsanity.com/paper/2406.02622/) (2024)
- [Building a Foundational Guardrail for General Agentic Systems via Synthetic Data](https://mlsanity.com/paper/2510.09781/) (2025)
- [Swiss Cheese Model for AI Safety: A Taxonomy and Reference Architecture for Multi-Layered Guardrails of Foundation Model Based Agents](https://mlsanity.com/paper/2408.02205/) (2024)
- [Understanding the Effects of Human-written Paraphrases in LLM-generated Text Detection](https://mlsanity.com/paper/2411.03806/) (2024)
- [SafeLLM: Unlearning Harmful Outputs from Large Language Models against Jailbreak Attacks](https://mlsanity.com/paper/2508.15182/) (2025)
- [TrustAI at SemEval-2024 Task 8: A Comprehensive Analysis of Multi-domain Machine Generated Text Detection Techniques](https://mlsanity.com/paper/2403.16592/) (2024)
- [Understanding the Effects of RLHF on the Quality and Detectability of LLM-Generated Texts](https://mlsanity.com/paper/2503.17965/) (2025)
- [Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs](https://mlsanity.com/paper/2604.07655/) (2026)

## Abstract (arXiv)

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. Our code is available at https://github.com/weiyezhimeng/Silent-Guardian.