Empirical Software Engineering TerraProbe: A Layered-Oracle Framework for Detecting Deceptive Fixes in LLM-Assisted Terraform
arXiv:2606.26590v1 Announce Type: new Abstract: Security misconfigurations in Terraform Infrastructure-as-Code are a growing risk in cloud deployments, and large language models are increasingly used as automated repair agents. Existing evaluations often treat a repair as successful when the targeted static-analysis finding disappears, without checking planning validity, behavioral change, or security intent. This paper presents TerraProbe, a five-layer oracle framework for evaluating LLM-assisted Terraform security repair. We apply TerraProbe to 288 first-pass repairs generated by gemini-2.5-
The increasing adoption of large language models for automated software repair, particularly in critical infrastructure-as-code like Terraform, necessitates robust evaluation frameworks to prevent the introduction of deceptive or insecure fixes.
This development addresses a critical vulnerability in the AI-driven automation of infrastructure management, ensuring that widespread adoption does not compromise security and stability.
The explicit recognition and systematic detection of 'deceptive fixes' in LLM-generated code repairs elevates the standard for AI-assisted security and emphasizes the need for validation beyond mere bug disappearance.
- · Cloud security vendors
- · DevOps teams
- · Security auditors
- · AI safety researchers
- · Malicious actors exploiting AI-generated vulnerabilities
- · Organizations relying solely on superficial AI repair validation
- · Unsecured LLM-assisted development tools
Improved security and reliability of cloud infrastructure managed with LLM assistance.
Increased demand for advanced AI auditing and validation tools, fostering a new cybersecurity sub-sector.
Potential for regulatory frameworks to mandate sophisticated validation measures for AI-generated code in critical systems.
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Read at arXiv cs.LG