Large language models (LLMs) are increasingly used to generate low-code workflows in platforms such as n8n, enabling rapid automation but also introducing structural flaws, unreachable paths, and mismatches between user intent and the produced workflow. We present VeriFlow, a multi-dimensional verification framework for LLM-generated workflows that combines three complementary analyses: (i) structural verification of workflow graphs, (ii) semantic verification that checks consistency between natural-language intent and workflow capabilities, ordering constraints, and parameter usage, and (iii) executable verification through a capability-aware sandbox that assesses reachability, parameter completeness, and execution readiness under a verification-oriented abstraction. VeriFlow produces interpretable diagnostic evidence, including missing capabilities, ordering inconsistencies, and parameter-level issues. Experiments on a benchmark of LLM-generated n8n workflows provide initial evidence that VeriFlow can reveal structural, semantic, and execution-related problems in an interpretable way, thereby supporting safer AI-assisted workflow engineering.