[2506.08837] Design Patterns for Securing LLM Agents against Prompt Injections

This paper proposes a set of principled design patterns that provide provable resistance to prompt injection attacks on LLM-based AI agents, systematically analyzing security-utility trade-offs and demonstrating real-world applicability through case studies. Prompt injection represents a critical vulnerability in agents with tool access or handling sensitive information, as attackers can exploit the agent's reliance on natural language inputs to manipulate behavior. The work establishes formal frameworks for building secure agents that maintain functionality while defending against these attacks.

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