用不可见干扰项让大模型输出看似合理实则错误的内容
TRAPDOC: Deceiving LLM Users by Injecting Imperceptible Phantom Tokens into Documents
- 在文档中注入人眼无法察觉的伪造标记
- 使大模型生成看似可信但实际错误的文本内容
- 适合关注AI滥用风险与用户引导的研究者
大语言模型在推理、写作、编辑和检索方面能力快速提升，带来越来越多功能的同时，也引发了社会担忧：用户过度依赖模型，将作业、任务或敏感文档处理完全交由模型完成，缺乏实质性参与。为缓解这一问题，我们提出一种在文档中注入不可见伪标记的技术，使大模型生成看似合理实则错误的输出。基于此，我们构建了TRAPDOC框架，旨在欺骗过度依赖模型的用户。通过实证评估，我们在多个商用大模型上验证了该框架的有效性，并与多种基线方法对比。TRAPDOC为促进用户更负责任、更深入地使用语言模型提供了坚实基础。代码已开源：https://github.com/jindong22/TrapDoc。
原文摘要 · Abstract (English)
The reasoning, writing, text-editing, and retrieval capabilities of proprietary large language models (LLMs) have advanced rapidly, providing users with an ever-expanding set of functionalities. However, this growing utility has also led to a serious societal concern: the over-reliance on LLMs. In particular, users increasingly delegate tasks such as homework, assignments, or the processing of sensitive documents to LLMs without meaningful engagement. This form of over-reliance and misuse is emerging as a significant social issue. In order to mitigate these issues, we propose a method injecting imperceptible phantom tokens into documents, which causes LLMs to generate outputs that appear plausible to users but are in fact incorrect. Based on this technique, we introduce TRAPDOC, a framework designed to deceive over-reliant LLM users. Through empirical evaluation, we demonstrate the effectiveness of our framework on proprietary LLMs, comparing its impact against several baselines. TRAPDOC serves as a strong foundation for promoting more responsible and thoughtful engagement with language models. Our code is available at https://github.com/jindong22/TrapDoc.
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