Abstract: Embodiments identify vulnerabilities in, e.g., a web application. An embodiment first, searches a database to identify payload characteristics for a Hypertext Transfer Protocol (HTTP) request associated with a uniform resource locator (URL) of a web application. In turn, one or more payloads with characteristics corresponding to the identified payload characteristics are obtained. Next, the HTTP request with the obtained one or more payloads is sent to the URL. Then, one or more responses to the HTTP request sent with the obtained one or more payloads are observed to determine if the web application includes one or more vulnerabilities.

Abstract: Embodiments determine configuration information pertaining to a compute layer, a virtualization layer, and a service layer of a computing workload. In an example embodiment, a machine learning engine interfaces with a workload deployed upon a network to initially determine file structures of the workload. The machine learning engine then compares the determined file structures of the workload with predefined representations of file structures stored in a classification database. In turn, the machine learning engine identifies configuration information pertaining to the workload based on the comparing.

Abstract: Embodiments identify vulnerabilities in, e.g., a web application. An embodiment first, searches a database to identify payload characteristics for a Hypertext Transfer Protocol (HTTP) request associated with a uniform resource locator (URL) of a web application. In turn, one or more payloads with characteristics corresponding to the identified payload characteristics are obtained. Next, the HTTP request with the obtained one or more payloads is sent to the URL. Then, one or more responses to the HTTP request sent with the obtained one or more payloads are observed to determine if the web application includes one or more vulnerabilities.