Relational databases are integral to modern information systems, serving as
the foundation for storing, querying, and managing data efficiently and
effectively. Advancements in large language modeling have led to the emergence
of text-to-SQL technologies, significantly enhancing the querying and
extracting of information from these databases and raising concerns about
privacy and security. Our research extracts the database schema elements
underlying a text-to-SQL model. Knowledge of the schema can make attacks such
as SQL injection easier. By asking specially crafted questions, we have
developed a zero-knowledge framework designed to probe various database schema
elements without knowledge of the database itself. The text-to-SQL models then
process these questions to produce an output that we use to uncover the
structure of the database schema. We apply it to specialized text-to-SQL models
fine-tuned on text-SQL pairs and generative language models used for SQL
generation. Overall, we can reconstruct the table names with an F1 of nearly
.75 for fine-tuned models and .96 for generative.

通过特别设计的问题，我们开发了一种零知识的框架，用于探测数据库模式的各种元素，从而揭示数据库模式的结构。我们将其应用于文本到 SQL 模型和生成语言模型，可以在细调模型中以近.75 的 F1 值和生成模型中以.96 的 F1 值重建表名。