Abstract: Dynamically generating monitoring tools for software applications, including: inspecting, using static code analysis, a non-executable representation of the application to identify one or more points in an application for monitoring; and for each of the one or more points in the application: generating a monitoring program; and inserting, into an executable representation of the application, the monitoring program at a location in the executable representation of the application that corresponds to the identified point in the application.

Abstract: An illustrative method for monitoring a cloud environment may include identifying, by at least one computing device and based on a scan of a cloud environment, a vulnerable software component in the cloud environment, determining, by the at least one computing device, an operational status for the vulnerable software component in the cloud environment, and generating, by the at least one computing device and based on the operational status for the vulnerable software component, an alert for the vulnerable software component.

Abstract: Dynamically generating monitoring tools for software applications, including: inspecting, using static code analysis, a non-executable representation of the application to identify one or more points in an application for monitoring; and for each of the one or more points in the application: generating a monitoring program; and inserting, into an executable representation of the application, the monitoring program at a location in the executable representation of the application that corresponds to the identified point in the application.

Abstract: To facilitate runtime monitoring and analysis of an application without modifying the actual application code, an agent monitors and analyzes an application through detection and evaluation of invocations of an API of a runtime engine provided for execution of the application. The agent registers to receive events which are generated upon invocation of target functions of the runtime engine API based on its load. Once loaded, the agent initially determines the language and language version number of the runtime engine. The agent determines associations of events for which to monitor and corresponding analysis code to execute upon detection of the invocations based on the language and version number information. When the agent detects an event during execution of the application based on invocations of the runtime engine API, the agent can monitor and analyze execution of the application based on execution of analysis code corresponding to the detected event.

Abstract: To support adding functionality to applications at a layer of abstraction above language-specific implementations of AOP, a language for implementing AOP facilitates runtime monitoring and analysis of an application independent of the language of the application. Aspects can be created for applications written in any supported language. Program code underlying implementations of aspects can be executed based on detecting triggering events during execution of the application. Routines written with the AOP language comprise event-based aspect code triggers that indicate an event which may occur during execution of the application and the associated aspect code to be executed. An agent deployed to a runtime engine to monitor the application detects events and evaluates contextual information about the detected events against the aspect triggers to determine if aspect code should be executed to perform further monitoring and analysis of the executing application.

Abstract: Presently described is a decompilation method of operation and system for parsing executable code, identifying and recursively modeling data flows, identifying and recursively modeling control flow, and iteratively refining these models to provide a complete model at the nanocode level. The nanocode decompiler may be used to determine if flaws, security vulnerabilities, or general quality issues exist in the code. The nanocode decompiler outputs in a standardized, human-readable intermediate representation (IR) designed for automated or scripted analysis and reporting. Reports may take the form of a computer annotated and/or partially human annotated nanocode listing in the above-described IR. Annotations may include plain English statements regarding flaws and pointers to badly constructed data structures, unchecked buffers, malicious embedded code or “trap doors,” and the like. Annotations may be generated through a scripted analysis process or by means of an expert-enhanced, quasi-autonomous system.

Abstract: Presently described is a decompilation method of operation and system for parsing executable code, identifying and recursively modeling data flows, identifying and recursively modeling control flow, and iteratively refining these models to provide a complete model at the nanocode level. The nanocode decompiler may be used to determine if flaws, security vulnerabilities, or general quality issues exist in the code. The nanocode decompiler outputs in a standardized, human-readable intermediate representation (IR) designed for automated or scripted analysis and reporting. Reports may take the form of a computer annotated and/or partially human annotated nanocode listing in the above-described IR. Annotations may include plain English statements regarding flaws and pointers to badly constructed data structures, unchecked buffers, malicious embedded code or “trap doors,” and the like. Annotations may be generated through a scripted analysis process or by means of an expert-enhanced, quasi-autonomous system.

Abstract: Presently described is a decompilation method of operation and system for parsing executable code, identifying and recursively modeling data flows, identifying and recursively modeling control flow, and iteratively refining these models to provide a complete model at the nanocode level. The nanocode decompiler may be used to determine if flaws, security vulnerabilities, or general quality issues exist in the code. The nanocode decompiler outputs in a standardized, human-readable intermediate representation (IR) designed for automated or scripted analysis and reporting. Reports may take the form of a computer annotated and/or partially human annotated nanocode listing in the above-described IR. Annotations may include plain English statements regarding flaws and pointers to badly constructed data structures, unchecked buffers, malicious embedded code or “trap doors,” and the like. Annotations may be generated through a scripted analysis process or by means of an expert-enhanced, quasi-autonomous system.

Abstract: Presently described is a decompilation method of operation and system for parsing executable code, identifying and recursively modeling data flows, identifying and recursively modeling control flow, and iteratively refining these models to provide a complete model at the nanocode level. The nanocode decompiler may be used to determine if flaws, security vulnerabilities, or general quality issues exist in the code. The nanocode decompiler outputs in a standardized, human-readable intermediate representation (IR) designed for automated or scripted analysis and reporting. Reports may take the form of a computer annotated and/or partially human annotated nanocode listing in the above-described IR. Annotations may include plain English statements regarding flaws and pointers to badly constructed data structures, unchecked buffers, malicious embedded code or “trap doors,” and the like.