Vulnerability Detection with Fine-grained Interpretations (2106.10478v1)

Abstract: Despite the successes of ML and deep learning (DL) based vulnerability detectors (VD), they are limited to providing only the decision on whether a given code is vulnerable or not, without details on what part of the code is relevant to the detected vulnerability. We present IVDetect an interpretable vulnerability detector with the philosophy of using AI to detect vulnerabilities, while using Intelligence Assistant (IA) via providing VD interpretations in terms of vulnerable statements. For vulnerability detection, we separately consider the vulnerable statements and their surrounding contexts via data and control dependencies. This allows our model better discriminate vulnerable statements than using the mixture of vulnerable code and~contextual code as in existing approaches. In addition to the coarse-grained vulnerability detection result, we leverage interpretable AI to provide users with fine-grained interpretations that include the sub-graph in the Program Dependency Graph (PDG) with the crucial statements that are relevant to the detected vulnerability. Our empirical evaluation on vulnerability databases shows that IVDetect outperforms the existing DL-based approaches by 43%--84% and 105%--255% in top-10 nDCG and MAP ranking scores. IVDetect correctly points out the vulnerable statements relevant to the vulnerability via its interpretation~in 67% of the cases with a top-5 ranked list. It improves over baseline interpretation models by 12.3%--400% and 9%--400% in accuracy.

• The paper introduces IntVD, which fuses graph convolutional networks and attention mechanisms to deliver fine-grained interpretability in vulnerability detection.
• It utilizes program dependency graphs and FA-GCN to analyze code context, yielding significant improvements in nDCG and MAP scores over conventional methods.
• The model also leverages GNNExplainer to highlight critical code segments, thus aiding developers in efficiently debugging and remediating vulnerabilities.

An Analysis of IntVD: Interpretable Vulnerability Detection with Fine-Grained Interpretations

The paper titled "Vulnerability Detection with Fine-Grained Interpretations" presents IntVD, an interpretable vulnerability detection (VD) model that enhances traditional ML and deep learning (DL) approaches. Unlike existing VD systems, IntVD provides users not only with the information about whether code is vulnerable but also with detailed insights into the specific parts of code contributing to detected vulnerabilities. This fine-grained interpretability is posited to greatly aid developers in pinpointing and resolving security issues in code.

The IntVD Approach

IntVD builds on the premise that understanding the context of vulnerability within the code is critical. The system leverages a methodology that considers both vulnerable statements and their broader code context through data and control dependencies. This is a significant enhancement over traditional VD systems that treat the code as a monolithic input, missing the opportunities to isolate the real sites of vulnerabilities from their context.

Core Methodologies

1. Graph-Based Code Representation: By modeling code representations as program dependency graphs (PDG), IntVD can decipher complex relationships and dependencies within software code that contribute to vulnerabilities. These graphs serve as the input for graph convolutional networks (GCN).
2. Feature-Attention Graph Convolutional Networks (FA-GCN): This model considers the diverse features of code fragments, integrating context-aware learning to improve vulnerability detection accuracy. FA-GCN leverages attention mechanisms to assign different levels of importance to various code features and dependencies, refining the detection and interpretation process.
3. Interpretable Machine Learning with GNNExplainer: To offer practical insights into vulnerabilities, IntVD uses GNNExplainer which highlights the crucial subgraphs in PDGs that are relevant to the detection outcomes. This interpretation method provides developers with a clear path for further investigation and remediation of vulnerabilities.

Empirical Findings

The paper conducts extensive experiments using three large C/C++ vulnerability datasets: Fan, Reveal, and FFMPeg+Qemu. The empirical results demonstrate that IntVD surpasses existing DL approaches in terms of nDCG and MAP scores, enhancing the precision and ranking of vulnerability detection substantially. Specifically, improvements range from 43% to 84% for nDCG scores and 105% to 255% for MAP scores. The model's ability to correctly identify vulnerable statements in 67% of cases within a top-5 ranked list also marks a significant advancement in interpretability.

Implications and Future Directions

The implications of this research are twofold. Practically, IntVD offers developers a tool that not only identifies vulnerabilities but also provides clear insights into their origins, thereby facilitating more effective debugging and patching processes. Theoretically, the integration of interpretable ML into VD systems sets a precedent for future research aiming to bridge the gap between detection and actionable insights.

Future directions could involve applying the IntVD framework to additional programming languages and enhancing the adaptability of the interpretation component to new kinds of vulnerabilities. Furthermore, integrating real-world developer feedback into the training process could refine the model's interpretative capabilities and make it more responsive to practical coding environments.

In conclusion, IntVD marks a meaningful stride towards more intelligent and interpretable VD systems, enhancing both detection efficacy and developer support through its dual-focus approach involving AI-driven vulnerability detection and interpretable ML. This synergy not only promises better accuracy but also provides a pathway towards more robust and secure software systems.