Defeating Prompt Injections by Design
Authors: name: Edoardo Debenedetti, name: Ilia Shumailov, name: Tianqi Fan, name: Jamie Hayes, name: Nicholas Carlini, name: Daniel Fabian, name: Christoph Kern, name: Chongyang Shi, name: Andreas Terzis, name: Florian Tramèr

Large Language Models (LLMs) are increasingly deployed in agentic systems
that interact with an external environment. However, LLM agents are vulnerable
to prompt injection attacks when handling untrusted data. In this paper we
propose CaMeL, a robust defense that creates a protective system layer around
the LLM, securing it even when underlying models may be susceptible to attacks.
To operate, CaMeL explicitly extracts the control and data flows from the
(trusted) query; therefore, the untrusted data retrieved by the LLM can never
impact the program flow. To further improve security, CaMeL relies on a notion
of a capability to prevent the exfiltration of private data over unauthorized
data flows. We demonstrate effectiveness of CaMeL by solving 67% of tasks
with provable security in AgentDojo [NeurIPS 2024], a recent agentic security
benchmark.