Modern Language Models (LMs) have been successfully employed in source code generation and understanding, leading to a significant increase in research focused on learning-based code intelligence, such as automated bug repair and test case generation. Despite their great potential, LMs for code intelligence (LM4Code) are susceptible to potential pitfalls , which hinder realistic performance and further impact their reliability and applicability in real-world deployment . Such challenges drive the need for a comprehensive understanding—not just identifying these issues but delving into their possible implications and existing solutions to build more reliable LMs tailored to code intelligence. Based on a well-defined systematic research approach, we conducted an extensive literature review to uncover the pitfalls inherent in LM4Code. Finally, 121 primary studies from top-tier venues have been identified. After carefully examining these studies, we designed a taxonomy of pitfalls in LM4Code research and conducted a systematic study to summarize the issues, current solutions, implications, and challenges of different pitfalls for LM4Code systems. We developed a comprehensive classification scheme that dissects pitfalls across four crucial aspects: data collection and labeling, system design and learning, performance evaluation, and deployment and maintenance. Through this study, we aim to provide a roadmap for researchers and practitioners, facilitating their understanding and utilization of LM4Code in reliable and trustworthy ways.

Machine learning-based Android malware classifiers struggle with concept drift: the rapid evolution of malware, especially with new families, can depress classification accuracy to near-random levels. Previous research has largely centered on detecting drift samples, with expert-led label revisions on these samples to guide model retraining. However, these methods often lack a comprehensive understanding of malware concepts and provide limited guidance for effective drift adaptation, leading to high human labeling costs.

Web shell detection has become increasingly crucial with the expansion of cloud computing, where automated malware analysis serves as a foundational approach. A key challenge in malware detection lies in balancing the reduction of false positives with maintaining detection accuracy amid rapid software ecosystem evolution. Existing methods require substantial expert intervention to mitigate false positives and often neglect the resource-intensive measures required to address model degradation caused by software updates. This study introduces ASTBAR, a novel method that extracts fine-grained AST fragments to distill benign behavioral knowledge from webserver software. By leveraging program structure and semantic analysis, ASTBAR generates fragment-level representations of benign samples and employs fragment matching to identify malware. Unlike prior techniques, ASTBAR achieves simultaneous improvements in precision, recall, and adaptability to software evolution. The evaluation results demonstrate that ASTBAR achieves an F1 score of$\mathbf{6 5. 3 5 \%}$, outperforming the state-of-theart methods by$\mathbf{1 0. 3 9 \%}$. In a$\mathbf{1 2}$-month industrial deployment spanning over one million users, ASTBAR maintained a 97.63% recall rat while reducing false positives by 700+ cases daily (equivalent to 30 expert hours).

Deep Neural Networks (DNNs) are susceptible to adversarial examples. Conventional attacks generate controlled noise-like perturbations that fail to reflect real-world scenarios and hard to interpretable. In contrast, recent unconstrained attacks mimic natural image transformations occurring in the real world for perceptible but inconspicuous attacks, yet compromise realism due to neglect of image post-processing and uncontrolled attack direction. In this paper, we propose RetouchUAA, an unconstrained attack that exploits a real-life perturbation: image retouching styles, highlighting its potential threat to DNNs. Compared to existing attacks, RetouchUAA offers several notable advantages. Firstly, RetouchUAA excels in generating interpretable and realistic perturbations through two key designs: the image retouching attack framework and the retouching style guidance module. The former custom-designed human-interpretability retouching framework for adversarial attack by linearizing images while modelling the local processing and retouching decision-making in human retouching behaviour, provides an explicit and reasonable pipeline for understanding the robustness of DNNs against retouching. The latter guides the adversarial image towards standard retouching styles, thereby ensuring its realism. Secondly, attributed to the design of the retouching decision regularization and the persistent attack strategy, RetouchUAA also exhibits outstanding attack capability and defense robustness, posing a heavy threat to DNNs. Experiments on ImageNet, Place365 and CUB200 reveal that RetouchUAA achieves nearly 100% white-box attack success against three DNNs, while achieving a better trade-off between image naturalness, transferability and defense robustness than baseline attacks.

Deep learning classifiers achieve state-of-the-art performance in various risk detection applications. They explore rich semantic representations and are supposed to automatically discover risk behaviors. However, due to the lack of transparency, the behavioral semantics cannot be conveyed to downstream security experts to reduce their heavy workload in security analysis. Although feature attribution (FA) methods can be used to explain deep learning, the underlying classifier is still blind to what behavior is suspicious, and the generated explanation cannot adapt to downstream tasks, incurring poor explanation fidelity and intelligibility.

In recent years, the underground economy is proliferating in the mobile system. These underground economy apps (UEware for short) make profits from providing non-compliant services, especially in sensitive areas (e.g., gambling, porn, loan). Unlike traditional malware, most of them (over 80%) do not have malicious payloads. Due to their unique characteristics, existing detection approaches cannot effectively and efficiently mitigate this emerging threat. To address this problem, we propose a novel approach to effectively and efficiently detect UEware by considering their UI transition graphs (UTGs). Based on the proposed approach, we design and implement a system, named DeUEDroid, to perform the detection. To evaluate DeUEDroid, we collect 25, 717 apps and build up the first large-scale ground-truth dataset (1, 700 apps) of UEware. The evaluation result based on the ground-truth dataset shows that DeUEDroid can cover new UI features and statically construct precise UTG. It achieves 98.22% detection F1-score and 98.97% classification accuracy, a significantly better performance than the traditional approaches. The evaluation result involving 24, 017 apps demonstrates the effectiveness and efficiency of UEware detection in real-world scenarios. Furthermore, the result also reveals that UEware are prevalent, i.e., 54% apps in the wild and 11% apps in the app stores are UEware. Our work sheds light on the future work of analyzing and detecting UEware. To engage the community, we have made our prototype system and the dataset available online.

Machine learning has shown promise for improving the accuracy of Android malware detection in the literature. However, it is challenging to (1) stay robust towards real-world scenarios and (2) provide interpretable explanations for experts to analyse. In this article, we proposeMsDroid, an Androidmalware detection system that makes decisions by identifyingmalicioussnippets with interpretable explanations. We mimic a common practice of security analysts, i.e., filtering APIs before looking through each method, to focus on local snippets around sensitive APIs instead of the whole program. Each snippet is represented with a graph encoding both code attributes and domain knowledge and then classified by Graph Neural Network (GNN). The local perspective helps the GNN classifier to concentrate on code highly correlated with malicious behaviors, and the information contained in graphs benefit in better understanding of the behaviors. Hence,MsDroidis more robust and interpretable in nature. To identify malicious snippets, we present a semi-supervised learning approach that only requires app labeling. The key insight is that malicious snippets only exist in malwares and appear at least once in a malware. To make malicious snippets less opaque, we design an explanation mechanism to show the importance of control flows and to retrieve similarly implemented snippets from known malwares. A comprehensive comparison with 5 baseline methods is conducted on a dataset of more than 81K apps in 3 real-world scenarios, includingzero-day,evolution, andobfuscation. The experimental results show thatMsDroidis more robust than state-of-the-art systems in all cases, with 5.37% to 49.52% advantage in F1-score. Besides, we demonstrate that the provided explanations are effective and illustrate how the explanations facilitate malware analysis.