This episode explores the vulnerabilities of AI-powered recommendation systems to attacks leveraging large language models (LLMs), based on a recent post from AIModels.fyi. We discuss how LLMs can be weaponized to undermine these systems and introduce the 'CheatAgent' framework.

• Are LLM-powered recommendation systems as secure as we think?

• How can attackers manipulate these systems in a 'black-box' environment?

• What role do prompt templates play in these attacks?

• Can user profiles be altered to skew recommendations?

• What is the 'CheatAgent' framework, and how does it work?

• What are the implications of LLMs being used as attack agents?

• How can we better protect these systems from sophisticated attacks?

• Where can I find this post from AIModels.fyi to read more?