LLM Platform Security: Applying a Systematic Evaluation Framework to
  OpenAI's ChatGPT Plugins
Umar Iqbal Tadayoshi Kohno Franziska Roesner
Large language model (LLM) platforms, such as ChatGPT, have recently begun
offering a plugin ecosystem to interface with third-party services on the
internet. While these plugins extend the capabilities of LLM platforms, they
are developed by arbitrary third parties and thus cannot be implicitly trusted.
Plugins also interface with LLM platforms and users using natural language,
which can have imprecise interpretations. In this paper, we propose a framework
that lays a foundation for LLM platform designers to analyze and improve the
security, privacy, and safety of current and future plugin-integrated LLM
platforms. Our framework is a formulation of an attack taxonomy that is
developed by iteratively exploring how LLM platform stakeholders could leverage
their capabilities and responsibilities to mount attacks against each other. As
part of our iterative process, we apply our framework in the context of
OpenAI's plugin ecosystem. We uncover plugins that concretely demonstrate the
potential for the types of issues that we outline in our attack taxonomy. We
conclude by discussing novel challenges and by providing recommendations to
improve the security, privacy, and safety of present and future LLM-based
computing platforms.