Abstract: A system includes a memory configured to store a software codebase and an instance of vulnerabilities associated with the software codebase. The system further includes a processor coupled to the memory and configured to access the software codebase and execute a scan to identify sets of metadata associated with the software codebase. The processor is configured to execute a first machine-learning model trained to generate a prediction of feature clusters based on the sets of metadata. The plurality of feature clusters includes a ratio of a vulnerability to a time-intensiveness associated with remediating the vulnerability. The processor is configured to execute a second machine-learning model trained to generate a prediction of associations between the plurality of feature clusters and the instance of vulnerabilities. The prediction of associations includes an estimate of a time-intensiveness for remediating the instance of vulnerabilities.

Abstract: A system for generating source code for a software application based on behavioral analysis of the software application is disclosed. The system obtains an input data stream provided to the software application and a corresponding output data stream generated by the software application. In response, the system determines a relationship and correlation between each series of inputs and the respective output and generates a set of input-output pairs. The system clusters each subset of input-output pairs that are associated with a specific function of the software application. The system generates a source code portion for each cluster of input-output pairs that are associated with a specific function of the software application. The system aggregates and finalizes the source code portions. The system executes the finalized source code.

Abstract: A system includes a memory configured to store a software codebase and an instance of vulnerabilities associated with the software codebase. The system further includes a processor coupled to the memory and configured to access the software codebase and execute a scan to identify sets of metadata associated with the software codebase. The processor is configured to execute a first machine-learning model trained to generate a prediction of feature clusters based on the sets of metadata. The plurality of feature clusters includes a ratio of a vulnerability to a time-intensiveness associated with remediating the vulnerability. The processor is configured to execute a second machine-learning model trained to generate a prediction of associations between the plurality of feature clusters and the instance of vulnerabilities. The prediction of associations includes an estimate of a time-intensiveness for remediating the instance of vulnerabilities.

Abstract: Systems, computer program products, and methods are described herein for dynamically performing linked security tests. The present invention may be configured to determine a fingerprint of an application, perform, in an order based on the fingerprint of the application, security test sequences on the application, parse responses of the application to the security test sequences to generate results of the security test sequences, and label, with the fingerprint, the results. The present invention may be further configured to provide, to one or more machine learning models, the labeled results to determine probabilities of applications having a same fingerprint as the fingerprint of the application failing the security test sequences, update, based on the probabilities of the applications failing the security test sequences, the order, and store, in a temporary persistent storage device and based on the updated order, the security test sequences.

Abstract: Systems, computer program products, and methods are described herein for dynamically generating linked security tests. The present invention may be configured to perform security tests on an application, generate, based on the results of the security tests, security test sequences that include at least one security test that the application failed, perform the security test sequences on the application, and, iteratively and until the application passes each security test sequence in an iteration, generate additional security test sequences. The present invention may be further configured to provide results of the security tests and security test sequences to one or more machine learning models to generate supplementary security test sequences and determine probabilities of the application failing the supplementary security test sequences.

Abstract: Systems, computer program products, and methods are described herein for dynamically generating linked security tests. The present invention may be configured to perform security tests on an application, generate, based on the results of the security tests, security test sequences that include at least one security test that the application failed, perform the security test sequences on the application, and, iteratively and until the application passes each security test sequence in an iteration, generate additional security test sequences. The present invention may be further configured to provide results of the security tests and security test sequences to one or more machine learning models to generate supplementary security test sequences and determine probabilities of the application failing the supplementary security test sequences.

Abstract: Systems, computer program products, and methods are described herein for dynamically generating linked security tests. The present invention may be configured to perform security tests on an application, generate, based on the results of the security tests, security test sequences that include at least one security test that the application failed, perform the security test sequences on the application, and, iteratively and until the application passes each security test sequence in an iteration, generate additional security test sequences. The present invention may be further configured to provide results of the security tests and security test sequences to one or more machine learning models to generate supplementary security test sequences and determine probabilities of the application failing the supplementary security test sequences.

Abstract: Artificial intelligence (“AI”) apparatus and methods are provided for hardening security of software applications. Under the conventional approaches, additional manual investment implementing security policies does not yield proportional increases in combating cyber security threats. Using manual approaches, it is increasingly difficult to consistently apply multiple policies covering different software applications or versions. This results in increased risk and technical debt. Over time, these undesirable consequences exacerbate the likelihood of inadvertently introducing an adverse policy omission or change. As the scale of software application deployed across and organization increases, it becomes even more difficult to ensure that security policies tracked and consistently applied. This may result in ineffective, contradictory or duplicative configuration requirements.

Abstract: Enhancement of web browser extension analysis capabilities, such as security application analysis, is realized by encapsulating the extension with a wrapper function that defines entry and exits points within the source code of the extension. By wrapping the web browser extension in a function that defines entry and exit points, the present invention enables the use of commercial SAST tools/engines and any other application which desires to analyze the web browser extension and/or extract data therefrom. The web browser extension is programmatically analyzed to identify the entry and exit points and, in response, the wrapper function is generated that defines the entry and exits points and the web browser extension is encapsulated with the wrapper function.

Abstract: Systems, computer program products, and methods are described herein for dynamically performing linked security tests. The present invention may be configured to determine a fingerprint of an application, perform, in an order based on the fingerprint of the application, security test sequences on the application, parse responses of the application to the security test sequences to generate results of the security test sequences, and label, with the fingerprint, the results. The present invention may be further configured to provide, to one or more machine learning models, the labeled results to determine probabilities of applications having a same fingerprint as the fingerprint of the application failing the security test sequences, update, based on the probabilities of the applications failing the security test sequences, the order, and store, in a temporary persistent storage device and based on the updated order, the security test sequences.

Abstract: Systems, computer program products, and methods are described herein for dynamically generating linked security tests. The present invention may be configured to perform security tests on an application, generate, based on the results of the security tests, security test sequences that include at least one security test that the application failed, perform the security test sequences on the application, and, iteratively and until the application passes each security test sequence in an iteration, generate additional security test sequences. The present invention may be further configured to provide results of the security tests and security test sequences to one or more machine learning models to generate supplementary security test sequences and determine probabilities of the application failing the supplementary security test sequences.

Abstract: Artificial intelligence (“AI”) apparatus and methods are provided for hardening security of software applications. Under the conventional approaches, additional manual investment implementing security policies does not yield proportional increases in combating cyber security threats. Using manual approaches, it is increasingly difficult to consistently apply multiple policies covering different software applications or versions. This results in increased risk and technical debt. Over time, these undesirable consequences exacerbate the likelihood of inadvertently introducing an adverse policy omission or change. As the scale of software application deployed across and organization increases, it becomes even more difficult to ensure that security policies tracked and consistently applied. This may result in ineffective, contradictory or duplicative configuration requirements.

Abstract: Enhancement of web browser extension analysis capabilities, such as security application analysis, is realized by encapsulating the extension with a wrapper function that defines entry and exits points within the source code of the extension. By wrapping the web browser extension in a function that defines entry and exit points, the present invention enables the use of commercial SAST tools/engines and any other application which desires to analyze the web browser extension and/or extract data therefrom. The web browser extension is programmatically analyzed to identify the entry and exit points and, in response, the wrapper function is generated that defines the entry and exits points and the web browser extension is encapsulated with the wrapper function.

Abstract: Systems, computer program products, and methods are described herein for testing an application with dynamically linked security tests. The present invention may be configured to perform, using a request engine, based on the first data, and based on test protocols stored in a first data structure, a first security test on an application. The present invention may be further configured to determine, based on determining that the application failed the first security test and based on a second data structure, whether the first security test is linked to one or more other security tests, where the second data structure includes security test sequences linking security tests and/or data to transmit from the first security test to the one or more other security tests, and provide, to the first queue, one or more other security tests to which the first security test is linked.