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arXiv · 2506.08837

Design Patterns for Securing LLM Agents against Prompt Injections

Luca Beurer-Kellner·Beat Buesser·Ana-Maria Creţu·Edoardo Debenedetti·Daniel Dobos·Daniel Fabian·Marc Fischer·David Froelicher·Kathrin Grosse·Daniel Naeff·Ezinwanne Ozoani·Andrew Paverd·Florian Tramèr·Václav Volhejn
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Abstract

As AI agents powered by Large Language Models (LLMs) become increasingly versatile and capable of addressing a broad spectrum of tasks, ensuring their security has become a critical challenge. Among the most pressing threats are prompt injection attacks, which exploit the agent's resilience on natural language inputs -- an especially dangerous threat when agents are granted tool access or handle sensitive information. In this work, we propose a set of principled design patterns for building AI agents with provable resistance to prompt injection. We systematically analyze these patterns, discuss their trade-offs in terms of utility and security, and illustrate their real-world applicability through a series of case studies.

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Luca Beurer-Kellner, Beat Buesser, Ana-Maria Creţu, Edoardo Debenedetti, Daniel Dobos, Daniel Fabian, Marc Fischer, David Froelicher, Kathrin Grosse, Daniel Naeff, Ezinwanne Ozoani, Andrew Paverd, Florian Tramèr, Václav Volhejn. 2025-06-27. Design Patterns for Securing LLM Agents against Prompt Injections. https://arxiv.org/abs/2506.08837

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