Text-to-Cypher translates natural-language questions into Cypher queries over property graphs. Graph database systems increasingly adopt this translation as a native query interface. Large language models generate fluent Cypher but stay unreliable. On Cypher-Bench a small model answers only 31.4% of questions correctly, and even a strong model plateaus at 71.76%. The dominant failure modes are structural rather than linguistic. For example, a generated query may parse correctly and follow the schema, yet still return zero rows because it traverses a relationship against the stored graph direction. To address this issue, we present CypherGuard, a training-free framework that detects and corrects these structural faults. Our approach uses two core components: a graph-native error taxonomy to guide repair, and a mined rule library to guide query generation. At inference time, CypherGuard first rewrites schema-detectable faults, then re-executes each draft and repairs it only when the query errors or returns nothing. On CypherBench test questions, across seven graphs, CypherGuard reaches 88.50% overall execution accuracy, +25.08% over the same-backbone zero-shot floor, surpassing an RL-fine-tuned 7B specialist and the strongest training-based system that we compare against.

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