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Recently, Graph Neural Network (GNN)-based vulnerability detection systems have achieved remarkable success. However, the lack of explainability poses a critical challenge to deploy black-box models in security-related domains. For this reason, several approaches have been proposed to explain the decision logic of the detection model by providing a set of crucial statements positively contributing to its predictions. Unfortunately, due to the weakly-robust detection models and suboptimal explanation strategy, they have the danger of revealing spurious correlations and redundancy issue. In this paper, we propose Coca, a general framework aiming to 1) enhance the robustness of existing GNN-based vulnerability detection models to avoid spurious explanations; and 2) provide both concise and effective explanations to reason about the detected vulnerabilities. \sysname consists of two core parts referred to as Trainer and Explainer. The former aims to train a detection model which is robust to random perturbation based on combinatorial contrastive learning, while the latter builds an explainer to derive crucial code statements that are most decisive to the detected vulnerability via dual-view causal inference as explanations. We apply Coca over three typical GNN-based vulnerability detectors. Experimental results show that Coca can effectively mitigate the spurious correlation issue, and provide more useful high-quality explanations., Comment: To appear in the Technical Track of ICSE 2024

The remarkable achievements of Artificial Intelligence (AI) algorithms, particularly in Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment across multiple sectors, including Software Engineering (SE). However, due to their black-box nature, these promising AI-driven SE models are still far from being deployed in practice. This lack of explainability poses unwanted risks for their applications in critical tasks, such as vulnerability detection, where decision-making transparency is of paramount importance. This paper endeavors to elucidate this interdisciplinary domain by presenting a systematic literature review of approaches that aim to improve the explainability of AI models within the context of SE. The review canvasses work appearing in the most prominent SE & AI conferences and journals, and spans 63 papers across 21 unique SE tasks. Based on three key Research Questions (RQs), we aim to (1) summarize the SE tasks where XAI techniques have shown success to date; (2) classify and analyze different XAI techniques; and (3) investigate existing evaluation approaches. Based on our findings, we identified a set of challenges remaining to be addressed in existing studies, together with a roadmap highlighting potential opportunities we deemed appropriate and important for future work., Comment: submitted to ACM Computing Surveys. arXiv admin note: text overlap with arXiv:2202.06840 by other authors

Java (de)serialization is prone to causing security-critical vulnerabilities that attackers can invoke existing methods (gadgets) on the application's classpath to construct a gadget chain to perform malicious behaviors. Several techniques have been proposed to statically identify suspicious gadget chains and dynamically generate injection objects for fuzzing. However, due to their incomplete support for dynamic program features (e.g., Java runtime polymorphism) and ineffective injection object generation for fuzzing, the existing techniques are still far from satisfactory. In this paper, we first performed an empirical study to investigate the characteristics of Java deserialization vulnerabilities based on our manually collected 86 publicly known gadget chains. The empirical results show that 1) Java deserialization gadgets are usually exploited by abusing runtime polymorphism, which enables attackers to reuse serializable overridden methods; and 2) attackers usually invoke exploitable overridden methods (gadgets) via dynamic binding to generate injection objects for gadget chain construction. Based on our empirical findings, we propose a novel gadget chain mining approach, \emph{GCMiner}, which captures both explicit and implicit method calls to identify more gadget chains, and adopts an overriding-guided object generation approach to generate valid injection objects for fuzzing. The evaluation results show that \emph{GCMiner} significantly outperforms the state-of-the-art techniques, and discovers 56 unique gadget chains that cannot be identified by the baseline approaches., Comment: To appear in the Technical Track of ICSE 2023

Java deserialization vulnerability is a severe threat in practice. Researchers have proposed static analysis solutions to locate candidate vulnerabilities and fuzzing solutions to generate proof-of-concept (PoC) serialized objects to trigger them. However, existing solutions have limited effectiveness and efficiency. In this paper, we propose a novel hybrid solution ODDFUZZ to efficiently discover Java deserialization vulnerabilities. First, ODDFUZZ performs lightweight static taint analysis to identify candidate gadget chains that may cause deserialization vulner-abilities. In this step, ODDFUZZ tries to locate all candidates and avoid false negatives. Then, ODDFUZZ performs directed greybox fuzzing (DGF) to explore those candidates and generate PoC testcases to mitigate false positives. Specifically, ODDFUZZ applies a structure-aware seed generation method to guarantee the validity of the testcases, and adopts a novel hybrid feedback and a step-forward strategy to guide the directed fuzzing. We implemented a prototype of ODDFUZZ and evaluated it on the popular Java deserialization repository ysoserial. Results show that, ODDFUZZ could discover 16 out of 34 known gadget chains, while two state-of-the-art baselines only identify three of them. In addition, we evaluated ODDFUZZ on real-world applications including Oracle WebLogic Server, Apache Dubbo, Sonatype Nexus, and protostuff, and found six previously unreported exploitable gadget chains with five CVEs assigned., Comment: To appear in the Main Track of IEEE S&P 2023