The article presents the results of fine-tuning large language models (LLMs) to detect vulnerabilities in source code. The authors used WizardCoder, an improvement of the state-of-the-art LLM StarCoder, and adapted…
Continue readingThe paper introduces a new kind of adversarial attack on machine learning models, specifically neural networks. It focuses on the vulnerability of these models to adversarial attacks. The proposed attack…
Continue readingThe article discusses the limitations of current vulnerability detection methods in Android applications, like the high number of false positives and limited analysis scope. It suggests that machine learning approaches…
Continue readingThe paper presents XGV-BERT, a new framework for software vulnerability detection. It combines the pre-trained CodeBERT model and Graph Neural Network (GCN) to achieve superior accuracy in detecting software vulnerabilities….
Continue readingTrusted Execution Environments (TEEs) embedded in IoT devices provide a deployable solution for securing IoT applications at the hardware level. However, Trusted OSes for IoT devices have received little security…
Continue readingThe study presents DefectHunter, a novel model for detecting software vulnerabilities. Traditional vulnerability detection methods are time-consuming, labor-intensive, and often underperform when dealing with complex datasets. The authors propose an…
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