Self-Disguise Attack: Induce the LLM to disguise itself for AIGT detection evasion

• URL: http://arxiv.org/abs/2508.15848v1
• Date: Wed, 20 Aug 2025 04:17:03 GMT
• Title: Self-Disguise Attack: Induce the LLM to disguise itself for AIGT detection evasion
• Authors: Yinghan Zhou, Juan Wen, Wanli Peng, Zhengxian Wu, Ziwei Zhang, Yiming Xue,
• Abstract summary: Self-Disguise Attack (SDA) is a novel approach that enables Large Language Models to actively disguise its output.<n>We show that the SDA effectively reduces the average detection accuracy of various AIGT detectors across texts generated by three different LLMs.
• Score: 16.94434185181644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-generated text (AIGT) detection evasion aims to reduce the detection probability of AIGT, helping to identify weaknesses in detectors and enhance their effectiveness and reliability in practical applications. Although existing evasion methods perform well, they suffer from high computational costs and text quality degradation. To address these challenges, we propose Self-Disguise Attack (SDA), a novel approach that enables Large Language Models (LLM) to actively disguise its output, reducing the likelihood of detection by classifiers. The SDA comprises two main components: the adversarial feature extractor and the retrieval-based context examples optimizer. The former generates disguise features that enable LLMs to understand how to produce more human-like text. The latter retrieves the most relevant examples from an external knowledge base as in-context examples, further enhancing the self-disguise ability of LLMs and mitigating the impact of the disguise process on the diversity of the generated text. The SDA directly employs prompts containing disguise features and optimized context examples to guide the LLM in generating detection-resistant text, thereby reducing resource consumption. Experimental results demonstrate that the SDA effectively reduces the average detection accuracy of various AIGT detectors across texts generated by three different LLMs, while maintaining the quality of AIGT.

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