Silent Guardian: Protecting Text from Malicious Exploitation by Large Language Models (2312.09669v7)

Abstract: The rapid development of 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.

• The paper introduces Silent Guardian, a truncation protection mechanism that forces LLMs to prematurely end text generation.
• The paper employs the Super Tailored Protection algorithm to optimize Truncation Protection Examples, achieving near-perfect protection across diverse LLM configurations.
• The paper demonstrates the framework's scalability and real-world applicability in addressing privacy and copyright concerns in AI-generated content.

Analysis of Silent Guardian: A Text Protection Mechanism Against LLMs

The paper "Silent Guardian: Protecting Text from Malicious Exploitation by LLMs" investigates the emerging need for text protection mechanisms in the context of LLMs. LLMs have the potential to perform multifaceted tasks, including natural conversation and information generation. However, these capabilities could be leveraged for malicious purposes, raising concerns about privacy and copyright violations. This paper introduces a novel approach, Silent Guardian (SG), to protect sensitive text content from being exploited by LLMs.

Silent Guardian is predicated on a new concept termed Truncation Protection Examples (TPE), which inhibits LLMs' ability to process certain text inputs. By modifying the protected text, SG induces LLMs to sample an end token prematurely, effectively terminating further text generation. This approach notably diverges from conventional copyright and privacy protection methodologies that have become less effective with the evolution of LLMs.

A critical component of SG is the Super Tailored Protection (STP) algorithm. STP is designed to optimize the construction of TPEs in a computationally efficient manner while preserving the semantic consistency of the original content. The optimization framework incorporates gradation-based methods to construct a protective layer within text inputs, ensuring LLMs cannot generate responses when faced with such protected inputs.

Experimental evaluations of the SG reveal promising protective capabilities across several LLM configurations, achieving near-perfect protection success rates in various testing scenarios. This protection retains its robustness even when deployed across different LLM architectures, illustrating potential applicability in real-world scenarios.

Key insights from this paper highlight the scalability of SG in adapting to texts of varying lengths and styles, offering flexibility in practical applications. While earlier methods focused primarily on traceability and access control, SG bypasses these limitations by disabling the generative functions of LLMs when confronted with protected texts.

The paper underscores the importance of developing security-focused AI applications that preemptively address risks associated with advanced LLMs. The proposed Silent Guardian framework can be extended to other media and model types, suggesting broader implications for privacy and security in AI.

Looking to the future, further research might explore how SG principles could be integrated into adaptive security policies for LLM training. Additionally, the problem of ensuring these protections maintain resilience against evolving adversarial techniques remains pertinent, demanding continuous advancements in algorithmic strategies like the STP method.

The paper presents Silent Guardian as an innovative framework specifically designed to mitigate the risks posed by LLMs' generative capabilities. This research fosters a broader dialogue on AI's role in ensuring secure communication channels in increasingly digitized environments.