Design Patterns for Securing LLM Agents against Prompt Injections (2506.08837v3)

Abstract: As AI agents powered by 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.

• The paper introduces six robust design patterns that significantly mitigate prompt injection attacks in LLM-based agents.
• It presents a structured methodology validated through ten practical case studies, balancing agent utility with security imperatives.
• The research underscores the importance of proactive security measures in AI system design to prevent malicious attacks.

Securing LLM Agents Against Prompt Injection Attacks

The paper "Design Patterns for Securing LLM Agents Against Prompt Injections" provides a comprehensive examination of addressing the security vulnerabilities posed by prompt injection attacks in AI agents that utilize LLMs. The researchers propose a set of design patterns to build AI agents with proven resistance to such attacks, offering a structured approach to mitigate risks while maintaining utility.

Artificial Intelligence agents, particularly those utilizing LLMs, are becoming integral parts of various software systems, serving diverse roles such as interpreting natural language instructions and executing tasks through external tools and APIs. However, inherent security challenges arise when these agents become susceptible to prompt injection attacks. These attacks involve embedding malicious instructions within benign user inputs, leading to unauthorized or harmful actions by the agent.

To defend against the spate of prompt injection vulnerabilities that continue to manifest, the authors propose a methodological framework comprising several design patterns. These are architectural configurations that, when applied, limit the extent to which agents can be commandeered via prompt injections. The paper outlines six primary patterns:

1. Action-Selector Pattern: The agent is restricted to choosing from a predefined set of possible actions, effectively acting as a controlled switch mechanism. This limits the agent's interaction capability and precludes processing unvetted data as actionable commands.
2. Plan-Then-Execute Pattern: This pattern involves the agent formulating a fixed plan of action before processing any potentially harmful input. While constraining arbitrary action execution, it still leaves room for instructed manipulations within the confines of the predefined actions.
3. LLM Map-Reduce Pattern: Borrowing from the MapReduce computational paradigm, this pattern involves the distributed processing of individual data fragments by isolated sub-agents. It reduces vulnerability by limiting the agent's exposure per data segment processed.
4. Dual LLM Pattern: This pattern operates on segregating agent roles between a robust privileged LLM responsible for action planning and a quarantined LLM that processes untrusted data without executing unsafe commands.
5. Code-Then-Execute Pattern: Agents draft formal code to accomplish tasks, differing from ad-hoc tool use planning. This pattern contributes to delineation by codifying task processes into verifiable scripts, which are then subjected to execution.
6. Context-Minimization Pattern: Here, the focus is on reducing the contextual data used by agents, systematically filtering out untrusted inputs before making decisions, thus shielding the agent from embedded prompt injections.

The paper further elucidates these patterns via ten case studies illustrating real-world applications ranging from customer service chatbots to software engineering assistants. Each paper highlights the balance between safeguarding agent processes and maintaining functionality, recognizing the persistent need for task-specific designs as opposed to generic security solutions for AI agents.

The implications of this research are multifaceted, affecting how AI systems are architected and suggesting a shift towards implementing security at the design stage rather than post-deployment. Effectively, these patterns require AI developers to consider security and utility concurrently, requiring a trade-off analysis for incorporating defensive mechanisms in tandem with maintaining agent performance.

This paper lays groundwork for a more systematic exploration of AI security within LLM frameworks, highlighting proceedings that are valuable for advancing both theoretical understanding and practical implementations in AI safety. For future exploration, developments in AI should rigorously build upon these findings, improving robustness against prompt injection attacks while enhancing agent adaptability and efficiency. The community is tasked with validating these purposes through empirical studies and adapting to evolving threats as LLMs continue to grow in complexity and capability.