DEV Community: 白海洋

Prompt Engineering Cannot Truly Secure LLM-Generated SQL

白海洋 — Sun, 24 May 2026 13:53:01 +0000

Recently, many teams are working on Text-to-SQL, ChatBI, or data analysis Agents. One underestimated issue is that SQL generated by LLMs should not directly enter production databases.
This article discusses: addressing the common misconception that "prompt rules can control generated SQL," and explaining why pre-execution validation is still necessary.
Key points:
Prompts can guide the model, but cannot enforce database security.
Generated SQL requires deterministic pre-execution validation.
The correct pattern is prompt guidance + parser/catalog/policy/audit checks.
Original link: https://www.dpriver.com/blog/prompt-engineering-cannot-secure-llm-generated-sql/?utm_source=dev&utm_medium=community&utm_campaign=ai_sql_governance_external_2026q2&utm_content=shenhuan_dev_prompt_engineering_cannot_secure_llm_generated_sql

10 Security Risks of Text-to-SQL Before Going to Production

白海洋 — Sun, 24 May 2026 13:52:06 +0000

Recently, many teams are working on Text-to-SQL, ChatBI, or data analysis Agents. One underestimated issue is that SQL generated by LLMs should not directly enter production databases.
This article discusses: for teams currently launching Text-to-SQL, ChatBI, or database Agents, here are 10 categories of risks that must be checked before going live.
Key points:
Text-to-SQL security is not just about SQL injection.
It also requires checking permissions, sensitive fields, high-cost queries, semantic errors, and auditing.
This article serves as a pre-launch readiness checklist.
Original link: https://www.dpriver.com/blog/text-to-sql-security-10-risks-before-production-deployment/?utm_source=dev&utm_medium=community&utm_campaign=ai_sql_governance_external_2026q2&utm_content=shenhuan_dev_text_to_sql_security_10_risks_before_production_deployment

Why Enterprises Should Not Let LLMs Execute SQL Directly?

白海洋 — Sun, 24 May 2026 13:04:56 +0000

Recently, many teams are working on Text-to-SQL, ChatBI, or data analysis Agents. One underestimated issue is that SQL generated by LLMs should not directly enter production databases.
This article discusses: a risk explanation for managers and architecture leaders: there must be a validation layer between LLMs and production databases.
Key points:
Allowing LLMs to execute SQL directly brings security, permission, cost, and audit risks.
Prompts are not enforcement mechanisms.
A deterministic SQL validation layer can transform generative SQL into a controllable process.
Original link: https://www.dpriver.com/blog/why-enterprises-should-not-let-llms-execute-sql-directly/?utm_source=dev&utm_medium=community&utm_campaign=ai_sql_governance_external_2026q2&utm_content=shenhuan_dev_why_enterprises_should_not_let_llms_execute_sql_directly

What is an LLM SQL Guard?

白海洋 — Sun, 24 May 2026 13:02:23 +0000

Recently, many teams are working on Text-to-SQL, ChatBI, or data analysis Agents. One underestimated issue is that SQL generated by LLMs should not directly enter production databases.
This article discusses: explaining LLM SQL Guard with clear definitions: why Text-to-SQL cannot rely solely on model generation, and why deterministic SQL checks are mandatory before execution.
Key points:
LLM-generated SQL may be syntactically correct but semantically incorrect or unsafe.
SQL Guard performs deterministic checks before execution by combining parser, catalog, policy, risk, and audit.
Suitable as an introductory article for AI data governance, ChatBI, and Text-to-SQL teams.
Original link: https://www.dpriver.com/blog/what-is-an-llm-sql-guard/?utm_source=dev&utm_medium=community&utm_campaign=ai_sql_governance_external_2026q2&utm_content=shenhuan_dev_what_is_an_llm_sql_guard