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Deyin Li

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AAAI Conference 2025 Conference Paper

AIA: Autoregression-Based Injection Attacks Against Text2SQL Models

Deyin Li
Xiang Ling
Changjiang Li
Xiang Chen
Chunming Wu

To facilitate understanding of users' diverse queries against the back-end databases in web applications, researchers have introduced Text-to-SQL (Text2SQL) models that can generate well-structured SQL queries from users' query texts in natural language. As the Text2SQL model decouples the user queries with the back-end databases, it inherently mitigates the SQL injection risk posed by inserting users' input into pre-written SQL queries. However, what security risks to web applications may be posed by Text2SQL models remains an open question. In this paper, we present a new attack framework, named Autoregression-based Injection Attacks (AIA), to evaluate the security risks of Text2SQL models. In particular, AIA makes target models generate attack payloads by constructing specific inputs and adjusting the input auto-regressively. Our evaluation demonstrates that AIA can cause Text2SQL models to generate target output by adversarial inputs with success rates of over 70% in most scenarios. The generated adversarial input has certain transferability in target Text2SQL models. Additionally, practice experiments show that AIA can make Text2SQL models extract user lists from databases and even delete data in databases directly.

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EAAI Journal 2024 Journal Article

Multi-label arrhythmia classification using 12-lead ECG based on lead feature guide network

Yuhao Cheng
Deyin Li
Duoduo Wang
Yuhang Chen
Lirong Wang

Background Multi-label arrhythmia classification plays a crucial role in the prevention and diagnosis of cardiac diseases. Intelligent analysis of multi-lead electrocardiogram (ECG) signals is the main method of classification. Existing methods have shown that different lead ECG signals contribute differently to the classification network. Methods In this paper, we propose a Lead Feature Guide Network (LFG-Net) to improve the feature representation capability of the network by focusing on the lead features with high contribution. Firstly, the lead features with high contributions are selected by the Shapley Additive exPlanations (SHAP) method. Secondly, the lead features are utilized to guide the classification model in bridging the gap between the selected features and the classification model by minimizing the difference in their losses. Finally, the network improves the accuracy of arrhythmia classification due to the extraction of more valid feature information. Conclusion The results demonstrated that the F1-score reached 0. 842 for classification on the China Physiological Signal Challenge (CPSC) 2018 dataset, which indicated the network outperformed other state-of-the-art methods. The parameters of the network during prediction are 1. 02 M. Through experiments, it can be verified that the effectiveness of the proposed method on arrhythmia classification with multi-label, and it can also be demonstrated that the lead features with high contributions can improve the network classification accuracy.

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