Treat LLM-Generated Terraform As Untrusted: an Exploratory Security Study and a Practitioner’s Verification Workflow

Large language models (LLMs) generate infrastructure-as-code (IaC) artifacts far less reliably than application code: on IaC-Eval, a Terraform benchmark, GPT-4-class models score about 19% pass@1 versus over 85% on Python benchmarks. This article asks a practitioner-facing question: when engineers accept LLM-generated Terraform at face value, how often is it insecure, and what should teams do? I synthesize 21 studies on LLM-assisted IaC and report an exploratory security study of 600 Terraform files generated by five models for 40 AWS tasks with no security instructions, scanned with two security scanners, Checkov and tfsec. 91% of files contained a misconfiguration and 68% a HIGH or CRITICAL one, varying sharply by model. Two thirds of surveyed studies never test security at all. The takeaway: treat default LLM-generated Terraform as untrusted input and verify it through a staged pipeline of scanning, policy checks, and human review before it reaches production.