2024/06/20 by Đorđe Klisura, Klisura, Đorđe, Anthony Rios +1 · 3 citations
Computer Science · Decision Sciences · #Computation and Language (cs.CL) #Cryptography and Data Security #Data Quality and Management #Distributed systems and fault tolerance #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2406.14545
openalex publication_date 2024/06/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Text-to-SQL systems empower users to interact with databases using natural language, automatically translating queries into executable SQL code. However, their reliance on database schema information for SQL generation exposes them to significant security vulnerabilities, particularly schema inference attacks that can lead to unauthorized data access or manipulation. In this paper, we introduce a novel zero-knowledge framework for reconstructing the underlying database schema of text-to-SQL models without any prior knowledge of the database. Our approach systematically probes text-to-SQL models with specially crafted questions and leverages a surrogate GPT-4 model to interpret the outputs, effectively uncovering hidden schema elements -- including tables, columns, and data types. We demonstrate that our method achieves high accuracy in reconstructing table names, with F1 scores of up to .99 for generative models and .78 for fine-tuned models, underscoring the severity of schema leakage risks. We also show that our attack can steal prompt information in non-text-to-SQL models. Furthermore, we propose a simple protection mechanism for generative models and empirically show its limitations in mitigating these attacks.