通过滥用容器的特权和能力(如 CAP_DAC_READ_SEARCH、SYS_ADMIN、SYS_MODULE 等),以及不安全的挂载配置(如 Docker Socket、Host /etc、Host /proc 文件系统),可以实现容器逃逸、后门植入和集群控制等安全风险。
总结:通过配置云服务(如阿里云、AWS、Azure)的不安全策略和权限设置,可以创建各种安全验证场景,用于测试和演示潜在的安全风险和漏洞,包括不安全传输、未使用 KMS 加密、SSRF 漏洞、IAM 提权、命令执行等。
Comparison of various deep learning methods (including but not limited to Graph Convolutional Neural Networks, Generative Adversarial Networks, referenced as DESC [1], scDeepCluster [2], scDMFK [3], scziDesk [4], scAIDE [5], scGMAI [6], scCAN [7], and scDCCA [8]) in data feature extraction and clustering performance evaluation on different test datasets (single-cell RNA sequencing data, spatial transcriptomics data, image data). Comparative analysis of the strengths and weaknesses of different methods.