Bias Beware: The Impact of Cognitive Biases on LLM-Driven Product Recommendations

The advent of Large Language Models (LLMs) has revolutionized product recommendation systems, yet their susceptibility to adversarial manipulation poses critical challenges, particularly in real-world commercial applications. Our approach is the first one to tap into human psychological principles, seamlessly modifying product descriptions, making these adversarial manipulations hard to detect. In this work, we investigate cognitive biases as black-box adversarial strategies, drawing parallels between their effects on LLMs and human purchasing behavior. Through extensive experiments on LLMs of varying scales, we reveal significant vulnerabilities in their use as recommenders, providing critical insights into safeguarding these systems.