ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection (2110.07317v3)

Abstract: Identifying vulnerabilities in the source code is essential to protect the software systems from cyber security attacks. It, however, is also a challenging step that requires specialized expertise in security and code representation. To this end, we aim to develop a general, practical, and programming language-independent model capable of running on various source codes and libraries without difficulty. Therefore, we consider vulnerability detection as an inductive text classification problem and propose ReGVD, a simple yet effective graph neural network-based model for the problem. In particular, ReGVD views each raw source code as a flat sequence of tokens to build a graph, wherein node features are initialized by only the token embedding layer of a pre-trained programming language (PL) model. ReGVD then leverages residual connection among GNN layers and examines a mixture of graph-level sum and max poolings to return a graph embedding for the source code. ReGVD outperforms the existing state-of-the-art models and obtains the highest accuracy on the real-world benchmark dataset from CodeXGLUE for vulnerability detection. Our code is available at: \url{https://github.com/daiquocnguyen/GNN-ReGVD}.

• The paper introduces ReGVD, which leverages graph-based representations of source code and GNN architectures to enhance vulnerability detection.
• The research incorporates residual connections and a novel readout mechanism combining sum and max pooling to improve local structure learning.
• Experimental results on the CodeXGLUE benchmark demonstrate that ReGVD outperforms baselines like Devign and CodeBERT with an accuracy of 63.69%.

Revisiting Graph Neural Networks for Vulnerability Detection

The paper "ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection" introduces ReGVD, a novel approach employing Graph Neural Networks (GNNs) to identify vulnerabilities in source code. The primary motivation behind this research is the current challenges faced by traditional vulnerability detection methods, particularly the reliance on feature engineering and the limits posed by pre-trained programming LLMs in handling local structures of code effectively.

Methodology

ReGVD considers vulnerability detection as an inductive text classification problem. It builds a graph from the source code where GNNs are adept at demonstrating their prowess. The graph is constructed by treating either unique tokens or indexed tokens as nodes and establishing edges based on token co-occurrences within a sliding window. Notably, ReGVD uses only the token embedding layer from pre-trained programming LLMs such as CodeBERT for node feature initialization, an approach that sidesteps extensive feature engineering.

The GNN framework is characterized by its utilization of both Graph Convolutional Networks (GCNs) and Gated GNNs, enhanced through residual connections to facilitate better gradient flow and avoid vanishing gradient problems. The graph-level readout mechanism integrates sum and max pooling to produce a comprehensive graph embedding, which is then passed through a softmax layer for the final classification task.

Experimental Evaluation

Extensive experiments are conducted on the CodeXGLUE vulnerability detection benchmark. Results highlight that ReGVD outperforms existing state-of-the-art models, including Devign, CodeBERT, and GraphCodeBERT, with a noteworthy accuracy of 63.69%. This underscores the model's strength in learning local structures and distinguishing between vulnerable and non-vulnerable code segments.

Key Findings

1. Graph Construction Techniques: The research explores different graph construction methodologies, showing that both unique token-focused and index-focused graphs contribute significantly to the model's success.
2. Residual Connection Efficacy: Incorporating residual connections among GNN layers proves essential for boosting model performance across various settings.
3. Superior Readout Mechanism: The adoption of a combination of sum and max pooling in the graph-level readout stage outperforms the Conv pooling layer utilized by baseline approaches like Devign.

Implications and Future Directions

The implications of this paper are profound for software security, particularly in automating the detection of vulnerabilities with minimal human intervention. The programming language-independence of the model implies its adaptability across diverse codebases, a critical requirement for broad application in heterogeneous software environments.

Future research could explore integrating more sophisticated pre-trained models and expanding the graph construction strategies to further augment the expressiveness of GNNs. Additionally, research could investigate the application of ReGVD to other source code analysis tasks, given its generalizable architecture.

In conclusion, this research represents a significant stride in vulnerability detection, leveraging the strengths of GNNs to address limitations inherent in traditional models and pre-trained LLMs. ReGVD stands as a testament to the potential of deep learning innovations in enhancing software security methodologies.