Author name cluster

Wen Cheng

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2025 Conference Paper

Security Attacks on LLM-based Code Completion Tools

Wen Cheng
Ke Sun
Xinyu Zhang
Wei Wang

The rapid development of large language models (LLMs) has significantly advanced code completion capabilities, giving rise to a new generation of LLM-based Code Completion Tools (LCCTs). Unlike general-purpose LLMs, these tools possess unique workflows, integrating multiple information sources as input and prioritizing code suggestions over natural language interaction, which introduces distinct security challenges. Additionally, LCCTs often rely on proprietary code datasets for training, raising concerns about the potential exposure of sensitive data. This paper exploits these distinct characteristics of LCCTs to develop targeted attack methodologies on two critical security risks: jailbreaking and training data extraction attacks. Our experimental results expose significant vulnerabilities within LCCTs, including a 99.4% success rate in jailbreaking attacks on GitHub Copilot and a 46.3% success rate on Amazon Q. Furthermore, We successfully extracted sensitive user data from GitHub Copilot, including 54 real email addresses and 314 physical addresses associated with GitHub usernames. Our study also demonstrates that these code-based attack methods are effective against general-purpose LLMs, highlighting a broader security misalignment in the handling of code by modern LLMs. These findings underscore critical security challenges associated with LCCTs and suggest essential directions for strengthening their security frameworks.

PDF Details DOI

JBHI Journal 2023 Journal Article

A Deep Learning Model for Automatic Segmentation of Intraparenchymal and Intraventricular Hemorrhage for Catheter Puncture Path Planning

Guoyu Tong
Xi Wang
Huiyan Jiang
Anhua Wu
Wen Cheng
Xiao Cui
Long Bao
Ruikai Cai

Intracerebral hemorrhage is the subtype of stroke with the highest mortality rate, especially when it also causes secondary intraventricular hemorrhage. The optimal surgical option for intracerebral hemorrhage remains one of the most controversial areas of neurosurgery. We aim to develop a deep learning model for the automatic segmentation of intraparenchymal and intraventricular hemorrhage for clinical catheter puncture path planning. First, we develop a 3D U-Net embedded with a multi-scale boundary aware module and a consistency loss for segmenting two types of hematoma in computed tomography images. The multi-scale boundary aware module can improve the model's ability to understand the two types of hematoma boundaries. The consistency loss can reduce the probability of classifying a pixel into two categories at the same time. Since different hematoma volumes and locations have different treatments. We also measure hematoma volume, estimate centroid deviation, and compare with clinical methods. Finally, we plan the puncture path and conduct clinical validation. We collected a total of 351 cases, and the test set contained 103 cases. For intraparenchymal hematomas, the accuracy can reach 96 $ \% $ when the proposed method is applied for path planning. For intraventricular hematomas, the proposed model's segmentation efficiency and centroid prediction are superior to other comparable models. Experimental results and clinical practice show that the proposed model has potential for clinical application. In addition, our proposed method has no complicated modules and improves efficiency, with generalization ability.

Details DOI

EAAI Journal 2023 Journal Article

Diagnosis of hepatocellular carcinoma using deep network with multi-view enhanced patterns mined in contrast-enhanced ultrasound data

Xiangfei Feng
Wenjia Cai
Rongqin Zheng
Lina Tang
Jianhua Zhou
Hui Wang
Jintang Liao
Baoming Luo

Hepatocellular carcinoma, representing the most frequent primary liver cancer, is a common cancer disease that is the fourth leading cause of cancer-related mortality worldwide. In comparison, non-hepatocellular carcinoma liver cancers often present different prognoses and require distinct management which makes the accurate discrimination between hepatocellular carcinoma and non-hepatocellular carcinoma malignant lesions in contrast-enhanced ultrasound data critical for precise intervention. However, different types of liver cancers have similar enhanced patterns against the perfusion stages that raise the difficulty in the classification of hepatocellular carcinoma with the other liver cancers, especially when the contrast-enhanced ultrasound data is collected from different imaging machines. To this end, this paper innovatively proposes to extract perfusion features from a multi-view learning procedure for obtaining the inherent distinguishing features among liver cancers, leading to a more precise deep model in differentiating the hepatocellular carcinoma from other malignant cases. In particular, the proposed network consists of two novel structures for learning the correlation information among the different views to enhance the robustness of the features and fuse them by reducing redundant information. The proposed method is verified on a multi-source dataset collected from 1241 participants and achieves an AUC value of 89% for classification performance. The experimental results demonstrate the effectiveness of the proposed method for the diagnosis of hepatocellular carcinoma with a multi-source contrast-enhanced ultrasound dataset and might provide an effective assistant for clinical radiologists in liver cancer differentiation.

Details DOI