8 accepted papers
The safe deployment of large language models (LLMs) necessitates comprehensive safety evaluations through red teaming. However, existing methods face challenges in managing semantic intricacies and optimizing the efficiency of the search process. To overcome these limitations, we propose Better Red…
The responsible deployment of Large Language Models (LLMs) necessitates rigorous safety evaluations. However, a critical challenge arises from inconsistencies between an LLM’s internal refusal decisions and external safety assessments, hindering effective validation. This paper introduces the concep
Deep learning models have revolutionized various domains but have also raised concerns regarding their security and reliability. Adversarial attacks and coverage-based testing have been extensively studied to assess and enhance the dependability of deep neural networks. However, current research in…
Deep learning technologies have demonstrated remarkable performance in vulnerability detection. Existing works primarily adopt a uniform and consistent feature learning pattern across the entire target set. While designed for general-purpose detection tasks, they lack sensitivity towards target code…
In code vulnerability detection tasks, a detector trained on a label-rich source domain fails to provide accurate prediction on new or unseen target domains due to the lack of labeled training data on target domains. Previous studies mainly utilize domain adaptation to perform cross-domain vulnerabi…
Code vulnerability detection is a fundamental and challenging task in the software security field. Existing research works aim to learn semantic information from the source code by utilizing NLP technologies. However, in vulnerability detection tasks, some vulnerable samples are very similar to non-…
Vision transformers (ViTs) have demonstrated impressive performance and stronger adversarial robustness compared to Convolutional Neural Networks (CNNs). On the one hand, ViTs' focus on global interaction between individual patches reduces the local noise sensitivity of images. On the other hand, th…
Deep neural networks have been demonstrated to be vulnerable to adversarial examples, which fool networks by adding human-imperceptible perturbations to benign examples. At present, the practical transfer-based black-box attacks are attracting significant attention. However, most existing transfer-b…