Large Language Models (LLMs) have achieved impressive performance in translating natural language queries into executable SQL. However, these systems remain prone to deceptive failures : generating syntactically valid SQL that executes but fails to capture the user's intent. In this work, we argue that further progress can be made by focusing specifically on verification. In this paper, we formalize Text-to-SQL verification as a standalone task: assessing whether a candidate SQL query semantically satisfies a natural language request, without access to ground-truth labels. We propose and compare two modular verification strategies: Round-Trip Critique , which reverse-translates SQL into natural language to detect semantic drift, and Synthetic Execution Consistency , which uses unit-test-like synthetic inputs to ground verification in execution results. Our evaluation shows that these methods provide a robust signal for identifying incorrect queries, successfully flagging 64% of errors from a state-of-the-art generator and outperforming standard error detectors. We demonstrate two critical applications: (1) auditing foundational benchmarks (Spider, BIRD and KaggleDBQA), revealing that over two-thirds of "generator failures" actually stem from flawed benchmark labels, and (2) enabling Selective Generation, where a system uses verification signals to abstain from answering when confidence is low. Our results show that this paradigm significantly improves the quality of deployed data interfaces by transforming silent failures into explicit abstentions that alert the user to the failure and give them a chance to correct it.
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