Software vulnerabilities pose significant risks to the security and integrity of software systems. Although prior studies have explored vulnerability detection using deep learning and pre-trained models, these approaches often fail to provide the detailed explanations necessary for developers to understand and remediate vulnerabilities effectively. The advent of large language models (LLMs) has introduced transformative potential due to their advanced generative capabilities and ability to comprehend complex contexts, offering new possibilities for addressing these challenges. In this paper, we propose LLMVulExp, an automated framework designed to specialize LLMs for the dual tasks of vulnerability detection and explanation. To address the challenges of acquiring high-quality annotated data and injecting domain-specific knowledge, LLMVulExp leverages prompt-based techniques for annotating vulnerability explanations and fine-tunes LLMs using instruction tuning with Low-Rank Adaptation (LoRA), enabling LLMVulExp to detect vulnerability types in code while generating detailed explanations, including the cause, location, and repair suggestions. Additionally, we employ a Chain-of-Thought (CoT) based key code extraction strategy to focus LLMs on analyzing vulnerability-prone code, further enhancing detection accuracy and explanatory depth.We conducted experiments across multiple vulnerability detection settings on three benchmark datasets, demonstrating the effectiveness of our method. This study highlights the feasibility of utilizing LLMs for real-world vulnerability detection and explanation tasks, providing critical insights into their adaptation and application in software security.

Recent studies have highlighted the limitations of message-passing based graph neural networks (GNNs), e.g., limited model expressiveness, over-smoothing, over-squashing, etc. To alleviate these issues, Graph Transformers (GTs) have been proposed which work in the paradigm that allows message passing to a larger coverage even across the whole graph. Hinging on the global range attention mechanism, GTs have shown a superpower for representation learning on homogeneous graphs. However, the investigation of GTs on heterogeneous information networks (HINs) is still under-exploited. In particular, on account of the existence of heterogeneity, HINs show distinct data characteristics and thus require different treatment. To bridge this gap, in this paper we investigate the representation learning on HINs with Graph Transformer, and propose a novel model named HINormer, which capitalizes on a larger-range aggregation mechanism for node representation learning. In particular, assisted by two major modules, i.e., a local structure encoder and a heterogeneous relation encoder, HINormer can capture both the structural and heterogeneous information of nodes on HINs for comprehensive node representations. We conduct extensive experiments on four HIN benchmark datasets, which demonstrate that our proposed model can outperform the state-of-the-art.

The prevalence of graph structures attracts a surge of investigation on graph data, enabling several downstream tasks such as multi-graph classification. However, in the multi-graph setting, graphs usually follow a long-tailed distribution in terms of their sizes, i.e., the number of nodes. In particular, a large fraction of tail graphs usually have small sizes. Though recent graph neural networks (GNNs) can learn powerful graph-level representations, they treat the graphs uniformly and marginalize the tail graphs which suffer from the lack of distinguishable structures, resulting in inferior performance on tail graphs. To alleviate this concern, in this paper we propose a novel graph neural network named SOLT-GNN, to close the representational gap between the head and tail graphs from the perspective of knowledge transfer. In particular, SOLT-GNN capitalizes on the co-occurrence substructures exploitation to extract the transferable patterns from head graphs. Furthermore, a novel relevance prediction function is proposed to memorize the pattern relevance derived from head graphs, in order to predict the complements for tail graphs to attain more comprehensive structures for enrichment. We conduct extensive experiments on five benchmark datasets, and demonstrate that our proposed model can outperform the state-of-the-art baselines.