The Challenge of Prompt Injection
Large Language Models (LLMs) are increasingly used as intelligent agents that can understand instructions, make plans, and execute actions using external tools. While powerful, this opens up new security vulnerabilities, with prompt injection being a primary threat.
Prompt injection occurs when malicious text manipulates an LLM's behavior, leading to unintended actions like data theft or unauthorized system changes. Traditional security can't easily handle these attacks because they exploit the very natural language interface that makes LLMs so useful.
This guide explores six design patterns from the paper "Design Patterns for Securing LLM Agents against Prompt Injections". These patterns offer practical, system-level strategies to build more secure LLM agents by intentionally constraining their capabilities, providing a trade-off between utility and security.
Mitigation Design Patterns
Select a pattern to see its explanation, diagram, and an interactive example.
Building Safer AI Agents
Securing general-purpose agents is still an open challenge, but by applying principled design patterns, we can build application-specific agents that are significantly more resilient to prompt injection. No single pattern is a silver bullet; the most robust solutions often combine multiple patterns to create layered defenses.
The key is to prioritize secure design, define clear trust boundaries, and intentionally limit an agent's capabilities to prevent harmful actions before they can occur.