This post demonstrates how Docker AI Sandboxes provide stronger protection against AI agent credential theft attacks than human-in-the-loop defenses. The author revisits a previously demonstrated multistage attack against Kiro, where a malicious MCP server silently exfiltrated AWS credentials. Inside a Docker AI Sandbox, this attack fails immediately because host credential files are never mounted into the isolated microVM environment, making them structurally unavailable rather than just policy-restricted. The post also shows that commonly suggested mitigations like kiroignore are insufficient, as attackers can bypass file-reading restrictions through shell execution. Additionally, Docker Sandboxes enforce configurable network policies that block non-HTTP traffic and log outbound connection attempts, providing a second defensive layer. The author notes remaining risks including credentials stored within the project directory, prompt injection, and full model API context exposure, but concludes these risks are more manageable and visible than the unmitigated attack surface.