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    <abstract>The technology of text-to-SQL has significantly enhanced the efficiency of accessing and manipulating databases. However, limited research has been conducted to study its vulnerabilities emerging from malicious user interaction. By proposing TrojanSQL, a backdoor-based SQL injection framework for text-to-SQL systems, we show how state-of-the-art text-to-SQL parsers can be easily misled to produce harmful SQL statements that can invalidate user queries or compromise sensitive information about the database. The study explores two specific injection attacks, namely boolean-based injection and union-based injection, which use different types of triggers to achieve distinct goals in compromising the parser. Experimental results demonstrate that both medium-sized models based on fine-tuning and LLM-based parsers using prompting techniques are vulnerable to this type of attack, with attack success rates as high as 99% and 89%, respectively. We hope that this study will raise more concerns about the potential security risks of building natural language interfaces to databases.</abstract>
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%0 Conference Proceedings
%T TrojanSQL: SQL Injection against Natural Language Interface to Database
%A Zhang, Jinchuan
%A Zhou, Yan
%A Hui, Binyuan
%A Liu, Yaxin
%A Li, Ziming
%A Hu, Songlin
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhang-etal-2023-trojansql
%X The technology of text-to-SQL has significantly enhanced the efficiency of accessing and manipulating databases. However, limited research has been conducted to study its vulnerabilities emerging from malicious user interaction. By proposing TrojanSQL, a backdoor-based SQL injection framework for text-to-SQL systems, we show how state-of-the-art text-to-SQL parsers can be easily misled to produce harmful SQL statements that can invalidate user queries or compromise sensitive information about the database. The study explores two specific injection attacks, namely boolean-based injection and union-based injection, which use different types of triggers to achieve distinct goals in compromising the parser. Experimental results demonstrate that both medium-sized models based on fine-tuning and LLM-based parsers using prompting techniques are vulnerable to this type of attack, with attack success rates as high as 99% and 89%, respectively. We hope that this study will raise more concerns about the potential security risks of building natural language interfaces to databases.
%R 10.18653/v1/2023.emnlp-main.264
%U https://aclanthology.org/2023.emnlp-main.264
%U https://doi.org/10.18653/v1/2023.emnlp-main.264
%P 4344-4359

Markdown (Informal)

[TrojanSQL: SQL Injection against Natural Language Interface to Database](https://aclanthology.org/2023.emnlp-main.264) (Zhang et al., EMNLP 2023)

• TrojanSQL: SQL Injection against Natural Language Interface to Database (Zhang et al., EMNLP 2023)

ACL

• Jinchuan Zhang, Yan Zhou, Binyuan Hui, Yaxin Liu, Ziming Li, and Songlin Hu. 2023. TrojanSQL: SQL Injection against Natural Language Interface to Database. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4344–4359, Singapore. Association for Computational Linguistics.