Stealthy Attack on Large Language Model based Recommendation
Jinghao Zhang Yuting Liu Qiang Liu Shu Wu Guibing Guo Liang Wang
Recently, the powerful large language models (LLMs) have been instrumental in
propelling the progress of recommender systems (RS). However, while these
systems have flourished, their susceptibility to security threats has been
largely overlooked. In this work, we reveal that the introduction of LLMs into
recommendation models presents new security vulnerabilities due to their
emphasis on the textual content of items. We demonstrate that attackers can
significantly boost an item's exposure by merely altering its textual content
during the testing phase, without requiring direct interference with the
model's training process. Additionally, the attack is notably stealthy, as it
does not affect the overall recommendation performance and the modifications to
the text are subtle, making it difficult for users and platforms to detect. Our
comprehensive experiments across four mainstream LLM-based recommendation
models demonstrate the superior efficacy and stealthiness of our approach. Our
work unveils a significant security gap in LLM-based recommendation systems and
paves the way for future research on protecting these systems.