SecureMCP: Policy-Enforced Defense Against Prompt Injection in LLM-Generated SQL for AIoT Databases

Abstract

The deployment of Large Language Model (LLM)-generated SQL in Artificial Intelligence of Things (AIoT) systems introduces critical security risks, as prompt injection attacks can manipulate LLMs into producing unauthorized queries that expose sensitive data or execute destructive operations. Existing Natural Language to SQL (NL2SQL) research targets query accuracy, while current Model Context Protocol (MCP) servers offer only SQL-level protection without fine-grained, role-based access control.

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@article{kim2026securemcpb,
  title = {{SecureMCP: Policy-Enforced Defense Against Prompt Injection in LLM-Generated SQL for AIoT Databases}},
  author = {Wonbae Kim and Hee-Kyong Yoo and Nammee Moon},
  year = {2026},
  month = aug,
  journal = {Applied Sciences},
  doi = {10.3390/app16167974},
  url = {https://www.semanticscholar.org/paper/39676372aa39d51366b632f3d18e809c3b05b77a}
}