AI Gateways: The Key to Solving Excessive Agency

Introduction to Excessive Agency and its Risks

The next generation of AI is characterized by agentic workflows powered by LLMs, automating tasks and integrating seamlessly with third-party tools and systems. 

However, this advancement comes with its own set of risks. Among the most pressing is the "Excessive Agency" vulnerability, as highlighted in the OWASP Top 10 for LLM applications. This vulnerability underscores the challenges of granting AI agents too much functionality, permissions, or autonomy, leaving systems exposed to potentially devastating misuse. Additionally, excessive agency imposes security challenges related to third-party API security use cases. Readers can explore this topic further in one of our latest blog posts, built on Gartner's research, (which recommends a reverse API gateway as an efficient way to mitigate these risks). 

In this post, we will delve into the origins and risks of Excessive Agency, outline prevention strategies, and demonstrate how infrastructure solutions like Lunar.dev’s AI Gateway provide a robust and scalable approach to managing these vulnerabilities.

What Is Excessive Agency?

Excessive Agency refers to the vulnerability that arises when an AI agent, driven by an LLM, is granted broad functionality, extensive permissions, or excessive autonomy. This often occurs in systems where agents can autonomously interact with APIs, databases, or other systems. The danger lies in the AI agent performing unauthorized or harmful actions due to ambiguous, manipulated, or adversarial inputs.

The root causes of Excessive Agency include:

• Excessive Functionality: Agents are equipped with tools or plugins that allow unnecessary actions.
• Excessive Permissions: Agents can access sensitive systems or data beyond their operational needs.
• Excessive Autonomy: High-impact actions are performed without sufficient human oversight.

For instance, an LLM-powered email assistant might have the capability to read, send, and delete emails. An adversarial prompt could exploit this functionality to extract sensitive information or delete critical emails without user consent.

How Excessive Agency Emerged

The rise of autonomous AI agents is the result of rapid advancements in LLMs, enabling agents to not only process language but also execute complex tasks through APIs, plugins, and system calls.

Initially, LLMs were used for passive tasks, such as answering questions or generating content. However, with the integration of tool usage, agents can now:

1. Invoke APIs dynamically.
2. Chain interactions across tools to complete workflows.
3. Perform autonomous decision-making, potentially bypassing human oversight.

While these capabilities increase efficiency, they also expand the attack surface. Poorly scoped permissions or overly generic tools amplify the risks, making the lack of control mechanisms a critical vulnerability.

Risks Associated with Excessive Agency

When AI agents possess excessive agency, the risks span the spectrum of confidentiality, integrity, and availability:

1. Confidentiality Risks: Agents can inadvertently or maliciously expose sensitive data.
2. Integrity Risks: Unauthorized modifications to databases or systems can lead to corrupt information (Example: Twilio’s research on AI Agent excessive permissions)
3. Availability Risks: Denial of service or resource exhaustion attacks can disrupt operations.

Specific attack scenarios include:

• Indirect Prompt Injection: Malicious prompts trick agents into performing unintended actions, such as exfiltrating data or executing harmful commands.
• Chained Attacks: An agent compromised at one step propagates the malicious effect across the workflow.
• Privilege Escalation: Over-permissive configurations enable agents to perform high-impact actions without checks.

Mitigation and Prevention Strategies: A Gateway-Centric Approach

Traditional methods to mitigate Excessive Agency—like limiting extensions, reducing functionality, and enforcing permissions—depend heavily on developers to anticipate vulnerabilities. While effective in isolation, these strategies struggle to scale or adapt to the complexities of real-world AI systems.

A more robust solution lies in infrastructure-level enforcement, enabled by an AI Gateway. This approach not only controls LLM traffic but also governs the actions generated by agents. Acting as a centralized “doorkeeper,” the AI Gateway enforces policies in real-time, ensuring every API call and action complies with strict security standards.

Why use an AI Gateway?

1. Holistic Enforcement: Instead of relying on developers to preemptively address risks, the AI Gateway provides real-time oversight and ensures consistent enforcement across all agents.
2. Centralized Control: A unified enforcement layer allows policies to apply across multiple agents, creating a single source of truth.
3. Simplified Security: By validating and sanitizing all outbound API calls, the gateway removes the need for agent-specific configurations. It can also integrate with identity systems to enforce granular user access controls.
4. Human-in-the-Loop Oversight: Real-time monitoring in the gateway’s control plane allows human operators to review and approve high-impact actions when needed.

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How Lunar.dev’s AI Gateway addresses the key mitigation strategies:

Unlike traditional controls applied at the application level, an AI Gateway introduces:

• Centralized Visibility: Monitor all AI-to-API interactions from a single control plane.
• Dynamic Policies: Update traffic, permission, and anomaly detection rules in real-time.
• Scalability: Handle diverse AI agents and tools without increasing operational complexity.

This infrastructure-first approach ensures that security policies are robust, adaptable, and easy to enforce across distributed environments.

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1. Rate Limiting and Traffic Control
   • Implement granular rate limits using Lunar’s rate limiting flow to prevent resource exhaustion and detect abnormal traffic patterns.
   • This ensures that agents cannot flood APIs with excessive requests, even if compromised.
2. Priority-Based Queuing
   • Use priority queue flows to prioritize critical requests over non-essential ones, ensuring system availability during attacks or traffic spikes.
3. Domain Access Control
   • Enforce strict policies on API endpoints and methods through domain access control flows. For example:
     • Block access to sensitive endpoints (e.g., POST /DELETE).
     • Restrict permissions based on business needs.

4. Custom Metrics and Anomaly Detection
   • Enable observability with custom metrics collection. This allows real-time monitoring of usage patterns and rapid detection of anomalies, such as sudden spikes in unauthorized actions.
5. Establish Quotas: 
   • Define usage quotas for different users or applications to control the extent of API access and prevent overuse.

Conclusion

Excessive Agency is a growing threat in the age of LLM-powered AI agents. As these agents gain more autonomy, the risks associated with over-permissioned or overly functional tools rise exponentially. While traditional mitigations provide some relief, the only scalable and robust solution lies in deploying the right infrastructure.

Lunar.dev’s AI Gateway stands out as a comprehensive solution, offering real-time controls that align with OWASP’s prevention strategies. By integrating Lunar.dev’s policies—rate limiting, priority queuing, domain access controls, and custom metrics—organizations can secure their AI systems against the risks of Excessive Agency.

To learn more about how Lunar.dev can protect your AI-powered systems, visit our documentation.

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