Cloud-native technologies have revolutionized application development, with Kubernetes emerging as the de facto standard platform for containerization and orchestration. Kubernetes manages applications through API objects called resources, where users declare desired states via resource definitions that are processed by controllers to reconcile system discrepancies. However, this resource-based architecture introduces resource injection vulnerabilities, where controllers perform privileged operations using user-controllable fields without adequate validation. Attackers can exploit these weaknesses by injecting malicious content into resource fields to achieve unauthorized access and privilege escalation.In this paper, we conduct the first comprehensive study on 125 resource injection vulnerabilities from 8,306 Kubernetes-related vulnerabilities across common databases. For all studied vulnerabilities, we investigate their vulnerable fields, root causes, privileged operations, exploitation conditions, and fixing strategies. Our study reveals many interesting findings that can guide the detection and mitigation of resource injection vulnerabilities, as well as the development of more secure cloud-native applications.

Deep Neural Network (DNN) models have notably improved the efficiency of machine learning tasks. However, their high storage and computational costs restrict their deployment on resource-limited embedded devices. Knowledge distillation (KD) has emerged as a promising approach for compressing DNN models. However, two challenges in KD, namely the capacity gap problem and the time-consuming redundancy problem, have hindered its performance and efficiency in compression. To alleviate these challenges, this paper proposes a novel framework, called Ability-Aware Knowledge Distillation (AAKD). AAKD introduces a knowledge sample selection strategy and an adaptive teacher switching strategy based on the dynamic awareness of the student’s ability. This enables the framework to automatically select suitable knowledge samples and teacher networks according to the increasing representation ability of students. Extensive experiments on different datasets and models have demonstrated that AAKD can enhance the performance of compact student models, significantly improve the efficiency of distillation, and lead to higher compression rates.