Identifying and Mitigating the Security Risks of Generative AI
Abstract
Every major technical invention resurfaces the dual-use dilemma—the new technology has the potential to be used for good as well as for harm. Generative AI (GenAI) techniques, such as large language models (LLMs) and diffusion models, have shown remarkable capabilities (e.g., in-context learning, code-completion, and text-to-image generation and editing). However, GenAI can be used just as well by attackers to generate new attacks and increase
the velocity and efficacy of existing attacks. This paper reports the findings of a workshop held at Google (co-organized by Stanford University and the University of Wisconsin-Madison) on the dual-use dilemma posed by GenAI. This paper is not meant to be comprehensive,
and reports on some of the interesting findings from the workshop. We discuss short-term and long-term goals for the community on this topic. We hope this paper provides a launching point on this important topic and provides interesting problems that the research community can work to address.
the velocity and efficacy of existing attacks. This paper reports the findings of a workshop held at Google (co-organized by Stanford University and the University of Wisconsin-Madison) on the dual-use dilemma posed by GenAI. This paper is not meant to be comprehensive,
and reports on some of the interesting findings from the workshop. We discuss short-term and long-term goals for the community on this topic. We hope this paper provides a launching point on this important topic and provides interesting problems that the research community can work to address.
Research Areas
