How to Build a Secure AI Agent With Docker Sandboxing and Prompt-Injection Defenses

A developer tutorial series on building AI agents from scratch has released a new installment focused on closing security gaps in agent harnesses. The guide introduces Docker sandboxing to isolate tool execution, limiting any runaway commands to only the project directory rather than the full host machine. It also adds prompt-injection defenses to prevent the model from treating external tool output as trusted instructions, along with strict JSON-schema validation for every tool input before execution. The approach organizes each security control into its own module — covering resource limits, secret management, session control, and audit logging — to make rules easier to audit and extend. The author argues that human-only oversight is insufficient, as fatigue or inattention can cause security issues to be overlooked, making machine-level safeguards essential.