Lessons from Defending Gemini Against Indirect Prompt Injections
Authors: name: Chongyang Shi; name: Sharon Lin; name: Shuang Song; name: Jamie Hayes; name: Ilia Shumailov; name: Itay Yona; name: Juliette Pluto; name: Aneesh Pappu; name: Christopher A. Choquette-Choo; name: Milad Nasr; name: Chawin Sitawarin; name: Gena Gibson; name: Andreas Terzis; name: John "Four" Flynn
Published: 2025-05-20 15:54:45

Abstract: Gemini is increasingly used to perform tasks on behalf of users, where
function-calling and tool-use capabilities enable the model to access user
data. Some tools, however, require access to untrusted data introducing risk.
Adversaries can embed malicious instructions in untrusted data which cause the
model to deviate from the user's expectations and mishandle their data or
permissions. In this report, we set out Google DeepMind's approach to
evaluating the adversarial robustness of Gemini models and describe the main
lessons learned from the process. We test how Gemini performs against a
sophisticated adversary through an adversarial evaluation framework, which
deploys a suite of adaptive attack techniques to run continuously against past,
current, and future versions of Gemini. We describe how these ongoing
evaluations directly help make Gemini more resilient against manipulation.