왜 AI 에이전트는 보안 샌드박스가 필요할까

Source: Dev.to

The “Happy Path” Trap

Most developer demos look like this:

agent = Agent(tools=[Browser(), FileSystem()])
agent.run("Research competitors")

This works perfectly for a single user. However, consider the following edge cases:

• User A’s agent calls fs.read_file('../.env').
• User B’s browser tool lands on a page with infinite scroll and consumes 8 GB of RAM.

The Three Horsemen of Agent Infrastructure

1. Isolation Leaks

Shared runtimes mean shared secrets. Without strict kernel‑level isolation, agents can snoop on each other. If Agent A sets an environment variable OPENAI_API_KEY, Agent B running in the same process might be able to read it.

2. Resource Exhaustion

LLMs love loops. One “thought loop” can spawn 1,000 requests or burn 100 % CPU, killing the server for everyone else. Traditional web servers have request timeouts (e.g., 30 s), but agentic tasks often need to run for 5–10 minutes.

3. Zombie Processes

If an agent crashes midway through a Selenium browser session, who cleans up the Chrome process? Over time, these “zombie” processes accumulate until the server falls over.

The Solution: Kubernetes‑Style Orchestration

We realized that agents are just containers that talk back. To run them safely, you need the same primitives that cloud providers use:

• Ephemeral Sandboxes – Every agent run spins up a fresh, isolated Firecracker microVM or container. No state leaks.
• Hard Limits – Cap CPU, RAM, and—critically—time. If an agent loops for 5 minutes, kill it.
• Egress Filtering – Prevent agents from scanning your internal network (e.g., 192.168.x.x). Block such traffic at the network level.

Conclusion

The transition from “demo” to “production” is all about handling failure modes. At Runctl, we believe developers should focus on the agent’s logic while the infrastructure handles isolation, scheduling, and cleanup.

This article was originally published on the Runctl Engineering Blog. We are building the K8s‑style runtime for autonomous agents—give it a try if you are scaling past localhost.