As more companies move from testing AI agents to deploying them live, one thing is clear: giving agents too much power without control can be risky. These agents work in long-lasting, complex environments, browsing the web, reading code repositories, running shell commands, calling APIs, and interacting with internal systems. While this makes them powerful, it also creates new security challenges. A recent interview with Jonathan Wall, CEO of Runloop, highlights this shift: “Agents should start with minimal access and only be granted capabilities in a controlled way.” This approach emphasizes the need for infrastructure that prioritizes least privilege, clear isolation, and transparency in execution.

Why Least Privilege Matters in Agent Security

Creating a safe environment for production agents begins with limiting what they can do by default. The environment should be isolated, with no inbound network access, no implicit permissions for tools, and restricted outbound connections. Containers are often used for isolation because they’re lightweight and efficient. However, containers share the same kernel as the host machine, which has historically been a weak point. There have been multiple vulnerabilities, such as CVE-2019-5736 and CVE-2022-0492, which allowed attackers to escape container boundaries and gain control over the host system.

To address this, many organizations are turning to microvirtual machines (microVMs). These provide a much stronger hardware-level barrier, reducing the risk that a compromised agent can break out and cause damage beyond its sandbox. The choice between containers and microVMs is not just about speed; it’s a risk decision. When dealing with untrusted code or sensitive operations, stronger isolation helps prevent a single breach from escalating into a larger security incident.

Understanding the Modern Threat Model for AI Agents

Traditional cloud systems handle predictable, deterministic requests. AI agents, on the other hand, process untrusted inputs and generate probabilistic responses. This makes them more vulnerable to prompt injections, where hidden instructions embedded in external content can override system prompts. In 2023, experiments showed that malicious web content could manipulate AI responses, revealing how fragile instruction boundaries can be.

Research has also shown that agents with broad credentials are at risk. For example, if an agent is given long-lived service accounts or wide-ranging permissions, a single compromise could lead to data leaks or credential theft. The danger increases when external content is treated as trusted. Proper credential management, along with strict controls on high-risk actions, is essential to reduce these risks.

Overall, designing AI agent infrastructure requires a layered approach. Combining isolation, strict network policies, credential management, and continuous monitoring helps contain potential failures. This multi-layered strategy ensures that even if one layer is breached, the overall system remains resilient and secure.

Inspired by

• https://www.infoworld.com/article/4161016/the-cookbook-for-safe-powerful-agents.html

Sources

• unsplash.com
• cio.com
• owasp.org
• paloaltonetworks.com
• owasp.org