Deep learning (DL) models are increasingly deployed in safety-critical applications such as face recognition, autonomous driving, and medical diagnosis. Despite their impressive accuracy, they remain vulnerable to adversarial examples - subtle perturbations that can cause incorrect predictions, i.e., the robustness issues. While adversarial training improves robustness against known attacks, it often fails to generalize to unseen or stronger threats, revealing a critical gap in robustness generalization. In this work, we propose a dual-model fuzzing framework to enhance generalized robustness in DL models. Central to our method is a lightweight metric, the Lagrangian Information Bottleneck (LIB), which guides entropy-based mutation toward semantically meaningful and high-risk regions of the input space. The executor uses a resistant model and a more error-prone vulnerable model; their prediction consistency forms the basis of agreement mining, a label-free oracle for isolating decision-boundary samples. To ensure fuzzing effectiveness, we further introduce a task-driven seed selection strategy (e.g., SSIM for vision) that filters out low-quality inputs. We implement a prototype, TWINFUZZ, and evaluate it on six benchmark datasets and nine DL models. Compared with state-of-the-art testing approaches, TWINFUZZ achieves superior improvements in both training-specific and generalized robustness.

Source code vulnerabilities represent a critical threat to software security, potentially leading to severe consequences such as data breaches and system failures. Traditional static analysis tools, while widely used, suffer from high false positive rates and struggle to adapt to the increasing complexity of modern software. Deep learning-based approaches hold promise for automated vulnerability detection, but they face challenges including limited dataset quality, inadequate feature extraction, and lack of precise vulnerability localization capabilities. To address these limitations, we propose MaliVD, a novel vulnerability detection method in source code that leverages a multi-modal attention mechanism. MaliVD not only identifies vulnerability types but also pinpoints the specific lines of code where vulnerabilities are triggered. The model extracts sequential, tree-based, and graph-based features from source code and employs specialized neural networks to learn these diverse representations. By strategically focusing on Points of Interest within the code, MaliVD effectively prioritizes potentially vulnerable code regions, enhancing both detection accuracy and localization precision. Experimental results show that when compared with eight advanced vulnerability detection models across three large datasets, MaliVD demonstrates superior vulnerability detection and localization capabilities, maintaining highF1scores and localization precision. Particularly on the ReliVul dataset, theF1score is improved by 21.82%, and the Top-5 localization accuracy is 18% higher than other methods, with lower false positives across six mainstream vulnerability types, validating MaliVD’s practical application value in real-world environments.

The Internet of Things (IoT) is widely used in various sectors but is often prone to vulnerabilities. With the proprietary nature of IoT devices, their source code and firmware are frequently unavailable for open review, rendering blackbox fuzzing a viable approach. However, the effectiveness of blackbox fuzzing is often challenging due to the lack of feedback, especially the information of code coverage. In this paper, we propose WingMuzz to provide blackbox fuzzing of IoT protocols with effective feedback. The key is to guide blackbox fuzzing by utilizing runtime information from greybox fuzzing on counterpart open-source code. This is based on our observation that IoT protocols and open-source code conform to the same specifications, indicating that inputs exploring different code regions on open-source code may also discover new coverage on IoT protocols. WingMuzz uses a two-dimensional fuzzing schedule to optimize the process of fuzzing IoT protocols. The first dimension involves scheduling open-source implementations, referred to as wingmates, so that similar ones are preferred to guide blackbox fuzzing. The second dimension utilizes coverage-guided greybox fuzzing to test open-source code. This solution can bridge the performance gap between blackbox fuzzing and greybox fuzzing on IoT protocols. We evaluate the performance of WingMuzz across eight IoT protocols and compare it with six widely-used blackbox fuzzers. On average, WingMuzz can discover 42.1%, 26.92%, 25.01%, 34.95%, 23.56% and 11.63% more edges than Boofuzz, Spike, Peach, Snipuzz, Pulsar and ChatAFL, respectively. Additionally, WingMuzz exposes 10 bugs in IoT protocols while other fuzzers expose no more than 3 bugs. It also exposes 2 new protocol vulnerabilities in IoT devices while other fuzzers cannot identify any.