How Docker Sandboxes Redefine Security Boundaries for AI Coding Agents

Docker Sandboxes introduce a structured isolation model for AI coding agents by running them inside microVMs with their own kernel and Docker Engine, limiting direct host access. The model offers three file-sharing modes — direct mount, clone, and mountless — each carrying different levels of risk depending on how much the agent can interact with the host repository. Credential exposure is reduced by routing provider keys through a host-side proxy, though the agent retains whatever authority those credentials grant. Network traffic defaults to a deny-by-allow policy, but preset allowlists like the 'Balanced' profile may still permit broad access that exceeds task requirements. Shared skill stores and MCP gateway configurations can also extend trust boundaries across sandboxes, making it critical to audit each capability boundary before deployment.