Excessive agency in AI systems creates serious risk when over-permissioned agents take harmful actions. An AI gateway addresses this with real-time controls and centralized enforcement, securing both LLM traffic and agent actions at scale.
Agentic workflows are powered by large language models (LLMs), and are being used by businesses to automate tasks and integrate directly with third-party tools and systems.
That capability comes with real risk. Among the most pressing is “excessive agency,” a vulnerability highlighted in the OWASP Top 10 for LLM applications. It describes what happens when an AI agent gets too much functionality, too many permissions, or too much autonomy, exposing systems to serious misuse. Excessive agency also raises third-party API security concerns.
This post covers where excessive agency comes from, the risks it creates, prevention strategies, and how infrastructure like an AI gateway gives you a scalable way to manage it.
What Is Excessive Agency?
Excessive agency is what happens when an AI agent, driven by an LLM, is granted broad functionality, wide permissions, or too much autonomy. It shows up most often in systems where agents interact autonomously with APIs, databases, or other systems. The danger is that the agent can take unauthorized or harmful actions in response to ambiguous, manipulated, or adversarial inputs.
The root causes include:
- Excessive Functionality: Agents get tools or plugins that allow unnecessary actions.
- Excessive Permissions: Agents can access sensitive systems or data beyond what their job requires.
- Excessive Autonomy: High-impact actions happen without enough human oversight.
For example, an LLM-powered email assistant that can read, send, and delete email creates an opening: an adversarial prompt could exploit that functionality to extract sensitive information or delete critical messages without the user’s consent.
How Excessive Agency Emerged
Autonomous AI agents are the result of rapid advances in LLMs, which now process language and execute complex tasks through APIs, plugins, and system calls.
LLMs started out handling passive tasks like answering questions or generating content. With tool use added, agents can now:
- Invoke APIs dynamically.
- Chain interactions across tools to complete workflows.
- Make autonomous decisions, sometimes bypassing human oversight.
These capabilities increase efficiency, but they also expand the attack surface. Poorly scoped permissions and overly generic tools make the lack of control mechanisms a critical vulnerability.
Risks Associated With Excessive Agency
When AI agents carry excessive agency, the risk spans confidentiality, integrity, and availability:
- Confidentiality Risks: Agents can expose sensitive data, intentionally or not.
- Integrity Risks: Unauthorized changes to databases or systems can corrupt information, as Twilio’s research on rogue AI agents accessing APIs.
- Availability Risks: Denial-of-service or resource-exhaustion attacks can disrupt operations.
Specific attack scenarios include:
- Indirect Prompt Injection: Malicious prompts trick agents into unintended actions, such as exfiltrating data or executing harmful commands.
- Chained Attacks: An agent compromised at one step spreads the damage across the workflow.
- Privilege Escalation: Over-permissive configurations let agents take high-impact actions without checks.
Mitigation and Prevention Strategies: A Gateway-Centric Approach
Traditional mitigations depend heavily on developers anticipating vulnerabilities ahead of time. They work in isolation but struggle to scale or adapt to real-world AI systems.
A more durable solution is infrastructure-level enforcement through an AI gateway. This approach controls LLM traffic and governs the actions agents take. Acting as a centralized checkpoint, the AI gateway enforces policy in real time, so every API call and action meets your security standards.
Why Use an AI Gateway?
- Holistic Enforcement: Real-time oversight applies consistent enforcement across every agent, rather than relying on developers to catch risks ahead of time.
- Centralized Control: One enforcement layer applies policy across every agent, giving you a single source of truth.
- Simplified Security: Validating and sanitizing every outbound API call removes the need for agent-specific configuration, and integrates with identity systems for granular access control.
- Human-in-the-Loop Oversight: Real-time monitoring in the gateway’s control plane lets your team review and approve high-impact actions when needed.
AI Gateway Features
- Rate Limiting and Traffic Control: Use granular rate limits to prevent resource exhaustion and catch abnormal traffic patterns. This keeps agents from flooding APIs with excessive requests, even if compromised.
- Priority-Based Queuing: Use priority queue flows to keep critical requests moving ahead of non-essential ones, protecting availability during attacks or traffic spikes.
- Domain Access Control: Enforce strict policy on API endpoints and methods through domain access control. For example, block access to sensitive endpoints like POST/DELETE, and restrict permissions based on business need.
- Custom Metrics and Anomaly Detection: Enable observability with custom metrics collection, so you can monitor usage patterns in real time and catch anomalies like sudden spikes in unauthorized actions quickly.
- Establish Quotas: Define usage quotas for different users or applications to control API access and prevent overuse.
Excessive agency is a growing threat as LLM-powered AI agents take on more autonomy. Traditional mitigations offer some relief, but the only scalable, durable fix is the right infrastructure.
An AI gateway offers real-time controls that align with OWASP’s prevention strategies. Layering in rate limiting, priority queuing, domain access controls, and custom metrics gives you a way to secure your AI systems against excessive agency risk.
Learn more about how Boomi governs AI agent traffic across your enterprise.