arXiv subjects

Triet Huynh Minh Le

Publications and source records attributed to Triet Huynh Minh Le.

12 recordsLinked to original sources

arXiv2026

Vulnerable Models in the AI Supply Chain: An Empirical Study of Adoption, Propagation, and Usage Patterns

The rapid integration of Artificial Intelligence (AI) models has established a complex supply chain, but many of these shared artifacts harbor severe vulnerabilities such as insecure serialization and architectural backdoors. While prior efforts have extensively scanned model registries to flag malicious artifacts, a critical gap remains in understanding how developers actually adopt and integrate these unsafe models into downstream software systems. We bridge this gap by conducting a large-scale empirical study on the usage and propagation of 16,977 known vulnerable models on HuggingFace flagged by InsightDB. We track their prevalence across GitHub repositories, software packages, fine-tuning chains, and deployed applications. Our analysis reveals that, despite being publicly flagged, these vulnerable models appear in over 21,000 code locations across more than 5,000 open-source repositories, with 3,757 distinct flagged models (22.1\%) actively referenced in at least one project. To understand how these models are integrated in code and identify recurring usage patterns, we categorize and analyze the recurring patterns surrounding them via a customized pipeline that combines Code Property Graphs extraction with a tailored Frequent Subgraph Mining approach. Across all patterns, we find no evidence of provenance verification or model sanitization logic, with developers frequently bypassing security parameters or delegating trust to permissive framework defaults. These findings underscore that scanning model files alone is insufficient, highlighting the urgent need for context-aware security analysis and stricter integration guardrails at the application layer of the AI supply chain.

cs.CR↗

arXiv2026

ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment

Although Graph Neural Networks (GNNs) have shown promise for smart contract vulnerability detection, they still face significant limitations. Homogeneous graph models fail to capture the interplay between control flow and data dependencies, while heterogeneous graph approaches often lack deep semantic understanding, leaving them susceptible to adversarial attacks. Moreover, most black-box models fail to provide explainable evidence, hindering trust in professional audits. To address these challenges, we propose ORACAL (Observable RAG-enhanced Analysis with CausAL reasoning), a heterogeneous multimodal graph learning framework that integrates Control Flow Graph (CFG), Data Flow Graph (DFG), and Call Graph (CG). ORACAL selectively enriches critical subgraphs with expert-level security context from Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs), and employs a causal attention mechanism to disentangle true vulnerability indicators from spurious correlations. For transparency, the framework adopts PGExplainer to generate subgraph-level explanations identifying vulnerability triggering paths. Experiments on large-scale datasets demonstrate that ORACAL achieves state-of-the-art performance, outperforming MANDO-HGT, MTVHunter, GNN-SC, and SCVHunter by up to 39.6 percentage points, with a peak Macro F1 of 91.28% on the primary benchmark. ORACAL maintains strong generalization on out-of-distribution datasets with 91.8% on CGT Weakness and 77.1% on DAppScan. In explainability evaluation, PGExplainer achieves 32.51% Mean Intersection over Union (MIoU) against manually annotated vulnerability triggering paths. Under adversarial attacks, ORACAL limits performance degradation to approximately 2.35% F1 decrease with an Attack Success Rate (ASR) of only 3%, surpassing SCVHunter and MANDO-HGT which exhibit ASRs ranging from 10.91% to 18.73%.

cs.LG↗

arXiv2026

Securing the AI Supply Chain: What Can We Learn From Developer-Reported Security Issues and Solutions of AI Projects?

The rapid growth of Artificial Intelligence (AI) models and applications has led to an increasingly complex security landscape. Developers of AI projects must contend not only with traditional software supply chain issues but also with novel, AI-specific security threats. However, little is known about what security issues are commonly encountered and how they are resolved in practice. This gap hinders the development of effective security measures for each component of the AI supply chain. We bridge this gap by conducting an empirical investigation of developer-reported issues and solutions, based on discussions from Hugging Face and GitHub. To identify security-related discussions, we develop a pipeline that combines keyword matching with an optimal fine-tuned distilBERT classifier, which achieved the best performance in our extensive comparison of various deep learning and large language models. This pipeline produces a dataset of 312,868 security discussions, providing insights into the security reporting practices of AI applications and projects. We conduct a thematic analysis of 753 posts sampled from our dataset and uncover a fine-grained taxonomy of 32 security issues and 24 solutions across four themes: (1) System and Software, (2) External Tools and Ecosystem, (3) Model, and (4) Data. We reveal that many security issues arise from the complex dependencies and black-box nature of AI components. Notably, challenges related to Models and Data often lack concrete solutions. Our insights can offer evidence-based guidance for developers and researchers to address real-world security threats across the AI supply chain.

cs.SE↗

arXiv2025

Software Vulnerability Management in the Era of Artificial Intelligence: An Industry Perspective

Artificial Intelligence (AI) has revolutionized software development, particularly by automating repetitive tasks and improving developer productivity. While these advancements are well-documented, the use of AI-powered tools for Software Vulnerability Management (SVM), such as vulnerability detection and repair, remains underexplored in industry settings. To bridge this gap, our study aims to determine the extent of the adoption of AI-powered tools for SVM, identify barriers and facilitators to the use, and gather insights to help improve the tools to meet industry needs better. We conducted a survey study involving 60 practitioners from diverse industry sectors across 27 countries. The survey incorporates both quantitative and qualitative questions to analyze the adoption trends, assess tool strengths, identify practical challenges, and uncover opportunities for improvement. Our findings indicate that AI-powered tools are used throughout the SVM life cycle, with 69% of users reporting satisfaction with their current use. Practitioners value these tools for their speed, coverage, and accessibility. However, concerns about false positives, missing context, and trust issues remain prevalent. We observe a socio-technical adoption pattern in which AI outputs are filtered through human oversight and organizational governance. To support safe and effective use of AI for SVM, we recommend improvements in explainability, contextual awareness, integration workflows, and validation practices. We assert that these findings can offer practical guidance for practitioners, tool developers, and researchers seeking to enhance secure software development through the use of AI.

cs.SE↗

arXiv2025

Toward Realistic Evaluations of Just-In-Time Vulnerability Prediction

Modern software systems are increasingly complex, presenting significant challenges in quality assurance. Just-in-time vulnerability prediction (JIT-VP) is a proactive approach to identifying vulnerable commits and providing early warnings about potential security risks. However, we observe that current JIT-VP evaluations rely on an idealized setting, where the evaluation datasets are artificially balanced, consisting exclusively of vulnerability-introducing and vulnerability-fixing commits. To address this limitation, this study assesses the effectiveness of JIT-VP techniques under a more realistic setting that includes both vulnerability-related and vulnerability-neutral commits. To enable a reliable evaluation, we introduce a large-scale public dataset comprising over one million commits from FFmpeg and the Linux kernel. Our empirical analysis of eight state-of-the-art JIT-VP techniques reveals a significant decline in predictive performance when applied to real-world conditions; for example, the average PR-AUC on Linux drops 98% from 0.805 to 0.016. This discrepancy is mainly attributed to the severe class imbalance in real-world datasets, where vulnerability-introducing commits constitute only a small fraction of all commits. To mitigate this issue, we explore the effectiveness of widely adopted techniques for handling dataset imbalance, including customized loss functions, oversampling, and undersampling. Surprisingly, our experimental results indicate that these techniques are ineffective in addressing the imbalance problem in JIT-VP. These findings underscore the importance of realistic evaluations of JIT-VP and the need for domain-specific techniques to address data imbalance in such scenarios.

cs.SE↗

arXiv2025

VulGuard: An Unified Tool for Evaluating Just-In-Time Vulnerability Prediction Models

We present VulGuard, an automated tool designed to streamline the extraction, processing, and analysis of commits from GitHub repositories for Just-In-Time vulnerability prediction (JIT-VP) research. VulGuard automatically mines commit histories, extracts fine-grained code changes, commit messages, and software engineering metrics, and formats them for downstream analysis. In addition, it integrates several state-of-the-art vulnerability prediction models, allowing researchers to train, evaluate, and compare models with minimal setup. By supporting both repository-scale mining and model-level experimentation within a unified framework, VulGuard addresses key challenges in reproducibility and scalability in software security research. VulGuard can also be easily integrated into the CI/CD pipeline. We demonstrate the effectiveness of the tool in two influential open-source projects, FFmpeg and the Linux kernel, highlighting its potential to accelerate real-world JIT-VP research and promote standardized benchmarking. A demo video is available at: https://youtu.be/j96096-pxbs

cs.SE↗

arXiv2025

LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurations

Security misconfigurations in Container Orchestrators (COs) can pose serious threats to software systems. While Static Analysis Tools (SATs) can effectively detect these security vulnerabilities, the industry currently lacks automated solutions capable of fixing these misconfigurations. The emergence of Large Language Models (LLMs), with their proven capabilities in code understanding and generation, presents an opportunity to address this limitation. This study introduces LLMSecConfig, an innovative framework that bridges this gap by combining SATs with LLMs. Our approach leverages advanced prompting techniques and Retrieval-Augmented Generation (RAG) to automatically repair security misconfigurations while preserving operational functionality. Evaluation of 1,000 real-world Kubernetes configurations achieved a 94\% success rate while maintaining a low rate of introducing new misconfigurations. Our work makes a promising step towards automated container security management, reducing the manual effort required for configuration maintenance.

cs.SE↗

arXiv2024

Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++

Background: The C and C++ languages hold significant importance in Software Engineering research because of their widespread use in practice. Numerous studies have utilized Machine Learning (ML) and Deep Learning (DL) techniques to detect software vulnerabilities (SVs) in the source code written in these languages. However, the application of these techniques in function-level SV assessment has been largely unexplored. SV assessment is increasingly crucial as it provides detailed information on the exploitability, impacts, and severity of security defects, thereby aiding in their prioritization and remediation. Aims: We conduct the first empirical study to investigate and compare the performance of ML and DL models, many of which have been used for SV detection, for function-level SV assessment in C/C++. Method: Using 9,993 vulnerable C/C++ functions, we evaluated the performance of six multi-class ML models and five multi-class DL models for the SV assessment at the function level based on the Common Vulnerability Scoring System (CVSS). We further explore multi-task learning, which can leverage common vulnerable code to predict all SV assessment outputs simultaneously in a single model, and compare the effectiveness and efficiency of this model type with those of the original multi-class models. Results: We show that ML has matching or even better performance compared to the multi-class DL models for function-level SV assessment with significantly less training time. Employing multi-task learning allows the DL models to perform significantly better, with an average of 8-22% increase in Matthews Correlation Coefficient (MCC). Conclusions: We distill the practices of using data-driven techniques for function-level SV assessment in C/C++, including the use of multi-task DL to balance efficiency and effectiveness. This can establish a strong foundation for future work in this area.

cs.SE↗

arXiv2024

Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration

Context: Machine learning (ML) and deep learning (DL) analyze raw data to extract valuable insights in specific phases. The rise of continuous practices in software projects emphasizes automating Continuous Integration (CI) with these learning-based methods, while the growing adoption of such approaches underscores the need for systematizing knowledge. Objective: Our objective is to comprehensively review and analyze existing literature concerning learning-based methods within the CI domain. We endeavour to identify and analyse various techniques documented in the literature, emphasizing the fundamental attributes of training phases within learning-based solutions in the context of CI. Method: We conducted a Systematic Literature Review (SLR) involving 52 primary studies. Through statistical and thematic analyses, we explored the correlations between CI tasks and the training phases of learning-based methodologies across the selected studies, encompassing a spectrum from data engineering techniques to evaluation metrics. Results: This paper presents an analysis of the automation of CI tasks utilizing learning-based methods. We identify and analyze nine types of data sources, four steps in data preparation, four feature types, nine subsets of data features, five approaches for hyperparameter selection and tuning, and fifteen evaluation metrics. Furthermore, we discuss the latest techniques employed, existing gaps in CI task automation, and the characteristics of the utilized learning-based techniques. Conclusion: This study provides a comprehensive overview of learning-based methods in CI, offering valuable insights for researchers and practitioners developing CI task automation. It also highlights the need for further research to advance these methods in CI.

cs.SE↗

arXiv2023

Systematic Literature Review on Application of Machine Learning in Continuous Integration

This research conducted a systematic review of the literature on machine learning (ML)-based methods in the context of Continuous Integration (CI) over the past 22 years. The study aimed to identify and describe the techniques used in ML-based solutions for CI and analyzed various aspects such as data engineering, feature engineering, hyper-parameter tuning, ML models, evaluation methods, and metrics. In this paper, we have depicted the phases of CI testing, the connection between them, and the employed techniques in training the ML method phases. We presented nine types of data sources and four taken steps in the selected studies for preparing the data. Also, we identified four feature types and nine subsets of data features through thematic analysis of the selected studies. Besides, five methods for selecting and tuning the hyper-parameters are shown. In addition, we summarised the evaluation methods used in the literature and identified fifteen different metrics. The most commonly used evaluation methods were found to be precision, recall, and F1-score, and we have also identified five methods for evaluating the performance of trained ML models. Finally, we have presented the relationship between ML model types, performance measurements, and CI phases. The study provides valuable insights for researchers and practitioners interested in ML-based methods in CI and emphasizes the need for further research in this area.

cs.SE↗

arXiv2023

Mitigating ML Model Decay in Continuous Integration with Data Drift Detection: An Empirical Study

Background: Machine Learning (ML) methods are being increasingly used for automating different activities, e.g., Test Case Prioritization (TCP), of Continuous Integration (CI). However, ML models need frequent retraining as a result of changes in the CI environment, more commonly known as data drift. Also, continuously retraining ML models consume a lot of time and effort. Hence, there is an urgent need of identifying and evaluating suitable approaches that can help in reducing the retraining efforts and time for ML models used for TCP in CI environments. Aims: This study aims to investigate the performance of using data drift detection techniques for automatically detecting the retraining points for ML models for TCP in CI environments without requiring detailed knowledge of the software projects. Method: We employed the Hellinger distance to identify changes in both the values and distribution of input data and leveraged these changes as retraining points for the ML model. We evaluated the efficacy of this method on multiple datasets and compared the APFDc and NAPFD evaluation metrics against models that were regularly retrained, with careful consideration of the statistical methods. Results: Our experimental evaluation of the Hellinger distance-based method demonstrated its efficacy and efficiency in detecting retraining points and reducing the associated costs. However, the performance of this method may vary depending on the dataset. Conclusions: Our findings suggest that data drift detection methods can assist in identifying retraining points for ML models in CI environments, while significantly reducing the required retraining time. These methods can be helpful for practitioners who lack specialized knowledge of software projects, enabling them to maintain ML model accuracy.

cs.SE↗

arXiv2023

SoK: Machine Learning for Continuous Integration

Continuous Integration (CI) has become a well-established software development practice for automatically and continuously integrating code changes during software development. An increasing number of Machine Learning (ML) based approaches for automation of CI phases are being reported in the literature. It is timely and relevant to provide a Systemization of Knowledge (SoK) of ML-based approaches for CI phases. This paper reports an SoK of different aspects of the use of ML for CI. Our systematic analysis also highlights the deficiencies of the existing ML-based solutions that can be improved for advancing the state-of-the-art.

cs.SE↗