TRAPDOC: Deceiving LLM Users by Injecting Imperceptible Phantom Tokens into Documents
This addresses the social issue of misuse in tasks like homework and sensitive document processing, offering a method to promote responsible engagement, though it is incremental as it builds on existing adversarial techniques.
The paper tackles the problem of over-reliance on LLMs by proposing TRAPDOC, a framework that injects imperceptible phantom tokens into documents to cause LLMs to generate plausible but incorrect outputs, with empirical evaluation showing effectiveness on proprietary LLMs.
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.