Design Patterns for Securing LLM Agents against Prompt Injections
This addresses a critical security challenge for AI agents handling sensitive information or tool access, representing an incremental improvement in securing existing systems.
The paper tackles the problem of securing AI agents powered by Large Language Models (LLMs) against prompt injection attacks, proposing a set of design patterns that provide provable resistance, as demonstrated through case studies.
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.