Comparing AI Agent Security Approaches

Published May 22, 2026 • 5 min read

As AI agents transition from read-only assistants to read-write operators, the industry is grappling with how to secure them. Currently, there are four primary approaches to agent security. Here is an honest look at where they excel and where they fail.

1. Manual Review (The Status Quo)

This involves asking the LLM to "review the code before running it," or requiring a human to explicitly approve every command.

• Pros: Zero setup. Relies on human intuition.
• Cons: Extremely slow. Wastes LLM context windows (and tokens) reading boilerplate code. Humans suffer from alert fatigue and quickly start blindly approving `git clone` requests.

2. Sandboxed Execution

Running the agent entirely within an isolated container (like a Docker container) and monitoring its runtime behavior.

• Pros: Catch-all safety net. If a destructive command fires, the container is destroyed, not your laptop.
• Cons: High latency and infrastructure cost. Prevents the agent from interacting natively with your local IDE and filesystem, which degrades the developer experience.

3. Publisher Allowlists

Only allowing the agent to install skills published by trusted vendors.

• Pros: High confidence in the code's safety.
• Cons: Extremely restrictive. Agents thrive on the open-source ecosystem. Limiting them to a handful of "verified" publishers drastically reduces their utility.

4. Static Analysis (Agent Skill Audit)

Scanning the raw code and documentation for malicious patterns *before* execution.

• Pros: Lightning fast. Costs zero LLM tokens. Allows developers to run their agents locally without complex sandboxing infrastructure while blocking explicit threats like prompt injection and exfiltration.
• Cons: Can generate false positives (flagging a legitimate shell script as "suspicious"). Cannot catch deep supply-chain attacks hidden in nested dependencies.

The Recommendation

We believe the ideal setup is layered. Static Analysis should be the first line of defense. It is cheap, fast, and catches the vast majority of low-effort attacks.

When static analysis returns an ALLOW, the agent proceeds immediately. When it returns a WARN, the agent pauses to ask for Human Review. When it returns a BLOCK, the agent halts entirely.