Whispers in the Machine: Confidentiality in LLM-integrated Systems

Large Language Models (LLMs) are increasingly augmented with external tools and commercial services into LLM-integrated systems. While these interfaces can significantly enhance the capabilities of the models, they also introduce a new attack surface. Manipulated integrations, for example, can exploit the model and compromise sensitive data accessed through other interfaces. While previous work primarily focused on attacks targeting a model's alignment or the leakage of training data, the security of data that is only available during inference has escaped scrutiny so far. In this work, we demonstrate the vulnerabilities associated with external components and introduce a systematic approach to evaluate confidentiality risks in LLM-integrated systems. We identify two specific attack scenarios unique to these systems and formalize these into a tool-robustness framework designed to measure a model's ability to protect sensitive information. Our findings show that all examined models are highly vulnerable to confidentiality attacks, with the risk increasing significantly when models are used together with external tools.