Embedded systems are plagued by various security vulnerabilities that can lead to severe attacks. Therefore, it is crucial to detect and mitigate these vulnerabilities quickly and accurately. In this paper, we survey existing research on vulnerability detection in embedded systems, with taint analysis as the central perspective. Specifically, we describe and categorize embedded systems, highlight the challenges posed by embedded systems for taint analysis, examine state-of-the-art techniques, and categorize dozens of tools in this field. Our goal is to provide stakeholders with a better understanding of potential solutions to enhance the security and reliability of embedded systems. Finally, we predict and discuss some future directions for taint analysis in embedded systems.
Universal Adversarial Perturbations (UAPs) differ from traditional image-specific perturbations in that they deceive target models across diverse instances using a single perturbation. Prior research has primarily focused on enhancing the transferability of non-targeted UAPs; however, these efforts fail to generate transferable UAPs capable of classifying images into a specific target class. To address this limitation, we propose a generative adversarial framework named CITP for generating cross-instance targeted perturbations. CITP leverages shared features among instances of the same class to produce perturbations that can be transferred to other instances within that class. The framework distinguishes between generated adversarial samples and images of the target class, enabling it to learn the label distribution of the target class. Additionally, CITP integrates a mid-level feature discriminator to improve the transferability of perturbations across different model architectures. Experimental results demonstrate that CITP exhibits exceptional transferability in cross-instance targeted attacks and achieves strong performance against four defense mechanisms. Notably, CITP extends beyond image data, enabling precise targeted attacks on video data as well.
