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Chunming Wu

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4 papers

2 author rows

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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ECAI Conference 2025 Conference Paper

SEWLT: Semantic Enhancement for Weak Semantics Low-Resource Languages Translation

Chunming Wu
Yueran Wang
Shanxiong Chen
Xiaoliang Li
Ruiyuan Li

Symbolic scripts carry deep cultural connotations and important historical values. However, due to their unique symbolic structures, linguistic characteristics of weak semantic association and scarce corpus resources, existing neural network machine translation techniques face challenges including insufficient semantic understanding, severe Out-of-Vocabulary issues, poor translation quality, and limited adaptability to semantic noise when handling their translation tasks. To solve this problem, we propose Semantic Enhancement for Weak Semantics Low-Resource Languages Translation method (SEWLT), using the translation task from Naxi Dongba to Chinese as a case study. Experimental results on a self-constructed Naxi Dongba-Chinese small-scale parallel corpus show excellent performance in terms of accuracy, fluency, and semantic fidelity. It not only provides technical support for the digital preservation and research of the Naxi Dongba script, but also provides an important reference for the research of machine translation of similar weak semantic low-resource languages.

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AAAI Conference 2023 Conference Paper

Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation

Guoqiang Sun
Yibin Shen
Sijin Zhou
Xiang Chen
Hongyan Liu
Chunming Wu
Chenyi Lei
Xianhui Wei

Cross-domain recommendation has attracted increasing attention from industry and academia recently. However, most existing methods do not exploit the interest invariance between domains, which would yield sub-optimal solutions. In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning. Specifically, we perform two levels of cross-domain contrastive learning: 1) instance-to-instance contrastive learning, 2) instance-to-cluster contrastive learning. Not only that, we also take into account users' multi-granularity and multi-view interests. With this paradigm, SITN can explicitly learn the invariant knowledge of interest clusters between domains and accurately capture users' intents and preferences. We conducted extensive experiments on a public dataset and a large-scale industrial dataset collected from one of the world's leading e-commerce corporations. The experimental results indicate that SITN achieves significant improvements over state-of-the-art recommendation methods. Additionally, SITN has been deployed on a micro-video recommendation platform, and the online A/B testing results further demonstrate its practical value. Supplement is available at: https://github.com/fanqieCoffee/SITN-Supplement.

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IS Journal 2014 Journal Article

How Effective Are the Prevailing Attack-Defense Models for Cybersecurity Anyway?

Daojing He
Sammy Chan
Yan Zhang
Chunming Wu
Bing Wang

Attack-defense models play an important role in the design of cybersecurity systems. Here, the authors review some traditional and prevailing attack-defense models along with their weaknesses. Then, they survey some recently proposed paradigm shifts to these models based on which more effective security strategies can be designed. Further, they provide some suggestions on how to adopt the new models, and present challenges that need to be addressed in this field.

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