AI Agent Sandboxing

The AI Agent Execution Problem

Modern AI agents are incredibly capable, but they're not perfect. When an AI agent executes code:

• It might misunderstand the task and run destructive commands
• Prompt injection attacks could make it execute malicious code
• It might install packages with security vulnerabilities
• Edge cases could cause infinite loops or resource exhaustion

Without sandboxing, any of these could compromise your system. With BoxLite, they're contained.

Compatible AI Agents

How to Use BoxLite with AI Agents

Integration is straightforward. Here's a Python example:

import boxlite
from anthropic import Anthropic

client = Anthropic()

async def execute_ai_code(prompt: str) -> str:
    # Get code from AI
    response = client.messages.create(
        model="claude-3-opus-20240229",
        messages=[{"role": "user", "content": prompt}]
    )
    code = extract_code(response)

    # Execute in isolated sandbox
    async with boxlite.CodeBox() as box:
        result = await box.run(code)
        return result.stdout

Security Guarantees

When AI code runs in BoxLite:

• Hardware isolation: Each sandbox runs in its own micro-VM with a separate kernel
• No host access: The sandbox cannot see or modify your host filesystem
• Network isolation: Controlled network access (enable only if needed)
• Resource limits: CPU and memory limits prevent resource exhaustion
• Timeout protection: Long-running code is automatically terminated

Why Not Docker?

Docker containers share the host kernel. Container escape vulnerabilities (like CVE-2024-21626) could let malicious AI-generated code break out to your host. BoxLite's VM boundary is much harder to escape.

Read the full BoxLite vs Docker comparison →