Hijacking Large Language Models via Adversarial In-Context Learning
Yao Qiang, Xiangyu Zhou, Dongxiao Zhu
In-context learning (ICL) has emerged as a powerful paradigm leveraging LLMs for specific tasks by utilizing labeled examples as demonstrations in the precondition prompts. Despite its promising performance, ICL suffers from instability with the choice and arrangement of examples. Additionally, crafted adversarial attacks pose a notable threat to the robustness of ICL. However, existing attacks are either easy to detect, rely on external models, or lack specificity towards ICL. To address these issues, this work introduces a novel transferable attack for ICL, aiming to hijack LLMs to generate the targeted response. The proposed LLM hijacking attack leverages a gradient-based prompt search method to learn and append imperceptible adversarial suffixes to the in-context demonstrations. Extensive experimental results on various tasks and datasets demonstrate the effectiveness of our LLM hijacking attack, resulting in a distracted attention towards adversarial tokens, consequently leading to the targeted unwanted outputs.