Manipulating Large Language Models to Increase Product Visibility
Aounon Kumar Himabindu Lakkaraju
Large language models (LLMs) are increasingly being integrated into search
engines to provide natural language responses tailored to user queries.
Customers and end-users are also becoming more dependent on these models for
quick and easy purchase decisions. In this work, we investigate whether
recommendations from LLMs can be manipulated to enhance a product's visibility.
We demonstrate that adding a strategic text sequence (STS) -- a carefully
crafted message -- to a product's information page can significantly increase
its likelihood of being listed as the LLM's top recommendation. To understand
the impact of STS, we use a catalog of fictitious coffee machines and analyze
its effect on two target products: one that seldom appears in the LLM's
recommendations and another that usually ranks second. We observe that the
strategic text sequence significantly enhances the visibility of both products
by increasing their chances of appearing as the top recommendation. This
ability to manipulate LLM-generated search responses provides vendors with a
considerable competitive advantage and has the potential to disrupt fair market
competition. Just as search engine optimization (SEO) revolutionized how
webpages are customized to rank higher in search engine results, influencing
LLM recommendations could profoundly impact content optimization for AI-driven
search services. Code for our experiments is available at
https://github.com/aounon/llm-rank-optimizer.