论文标题
关于文本到SQL模型的安全漏洞
On the Security Vulnerabilities of Text-to-SQL Models
论文作者
论文摘要
尽管已经证明自然语言处理（NLP）算法容易受到故意攻击，但这种弱点是否会导致软件安全威胁的问题不足。为了弥合这一差距，我们对文本到SQL系统进行了漏洞测试，这些系统通常用于创建与数据库的自然语言接口。我们表明，可以操纵六个商业应用程序中的文本到SQL模块，以产生恶意代码，可能导致数据泄露和拒绝服务攻击。这是第一次证明NLP模型可以被利用为野外攻击媒介。此外，使用四种开源语言模型进行的实验证实了对文本到SQL系统的直接后门攻击实现100％的成功率，而不会影响其性能。这项工作的目的是吸引社区注意与NLP算法相关的潜在软件安全问题，并鼓励探索减轻它们的方法。
Although it has been demonstrated that Natural Language Processing (NLP) algorithms are vulnerable to deliberate attacks, the question of whether such weaknesses can lead to software security threats is under-explored. To bridge this gap, we conducted vulnerability tests on Text-to-SQL systems that are commonly used to create natural language interfaces to databases. We showed that the Text-to-SQL modules within six commercial applications can be manipulated to produce malicious code, potentially leading to data breaches and Denial of Service attacks. This is the first demonstration that NLP models can be exploited as attack vectors in the wild. In addition, experiments using four open-source language models verified that straightforward backdoor attacks on Text-to-SQL systems achieve a 100% success rate without affecting their performance. The aim of this work is to draw the community's attention to potential software security issues associated with NLP algorithms and encourage exploration of methods to mitigate against them.
