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submitted 3 days ago by remixtures@tldr.nettime.org to c/cybersecurity@fedia.io

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"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."

https://arxiv.org/html/2506.08837v2

#AI #GenerativeAI #LLMs #PromptInjection #AIAgents #AgenticAI #CyberSecurity

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[–] remixtures@tldr.nettime.org 1 points 3 days ago

"Design Patterns for Securing LLM Agents against Prompt Injections (2025) by 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, and Václav Volhejn.

I’m so excited to see papers like this starting to appear. I wrote about Google DeepMind’s Defeating Prompt Injections by Design paper (aka the CaMeL paper) back in April, which was the first paper I’d seen that proposed a credible solution to some of the challenges posed by prompt injection against tool-using LLM systems (often referred to as “agents”).

This new paper provides a robust explanation of prompt injection, then proposes six design patterns to help protect against it, including the pattern proposed by the CaMeL paper."

https://simonwillison.net/2025/Jun/13/prompt-injection-design-patterns/