An Early Categorization of Prompt Injection Attacks on Large Language
  Models
Sippo Rossi Alisia Marianne Michel Raghava Rao Mukkamala Jason Bennett Thatcher
Large language models and AI chatbots have been at the forefront of
democratizing artificial intelligence. However, the releases of ChatGPT and
other similar tools have been followed by growing concerns regarding the
difficulty of controlling large language models and their outputs. Currently,
we are witnessing a cat-and-mouse game where users attempt to misuse the models
with a novel attack called prompt injections. In contrast, the developers
attempt to discover the vulnerabilities and block the attacks simultaneously.
In this paper, we provide an overview of these emergent threats and present a
categorization of prompt injections, which can guide future research on prompt
injections and act as a checklist of vulnerabilities in the development of LLM
interfaces. Moreover, based on previous literature and our own empirical
research, we discuss the implications of prompt injections to LLM end users,
developers, and researchers.