Abstract: An initial corpus of source code is received. The initial corpus of source code is for training an Artificial Intelligence (AI) algorithm that generates source code. The initial corpus of source code is scanned, using a test suite, to identify one or more potential vulnerabilities in the initial corpus of the source code. The identified one or more potential vulnerabilities in the initial corpus of the source code are mitigated to produce a training corpus of source code. For example, the mitigation may comprise removing malware from the initial corpus. The mitigation is to remove the vulnerabilities so that the vulnerabilities do not show up in source code generated by the AI algorithm. The AI algorithm is then trained using the training corpus of source code. The trained AI algorithm is executed to produce generated source code.

Abstract: A defect in the code of a software program is identified. An initial ranking for the defect in the code of the software program is determined. One or more network websites are crawled to identify information associated with the defect in the code of the software program. The information associated with the defect in the defect in the code of the software program is analyzed. In response to analyzing the information associated with the defect in the code of the software program, a second ranking is created for the defect in the code of the software program. The defects in the code of the software program and the second ranking are generated for display in a graphical user interface. By prioritizing which defects are more critical, the quality of the released software improved. In addition, the released software is more secure because critical defects have been removed.

Abstract: Input source code is retrieved. The input source code is subject to one or more licenses. For example, input source code subject to the MIT and GPL V2 open-source licenses may be retrieved from an open-source repository. A code generation Artificial Intelligence (AI) algorithm is trained using the input source code. The trained code generation AI algorithm is executed to produce output source code. For example, a set of parameters that define the output source code may be provided as input to execute the code generation AI algorithm. One or more licenses associated with the output source code are identified. For example, a vector-based AI algorithm may be used to identify the one or more licenses. The one or more licenses are associated with the output source code. This allows for proper licensing and attribution of the output source code.

Abstract: A computer system includes a database, communication interface, and processor(s). The processor(s) is programmed to receive a consent message from a cardholder computing device. The consent message includes cardholder consent information indicating consent to one or more data services. The processor(s) is also programmed to receive transaction card details from the cardholder computing device. The transaction card details correspond to a transaction card of a cardholder and are associated with a financial account of the cardholder. Based on the transaction card details, the processor(s) generates a digital access token. The digital access token is associated with the financial account and the cardholder consent information. The processor(s) stores the digital access token in the database, transmits a cardholder authentication request message to an issuer, and receives an authentication ID from the cardholder computing device.

Abstract: A computer system includes a database, a communication interface, and a processor(s). The processor(s) is programmed to receive a consent message from a cardholder computing device. The consent message includes cardholder consent information indicating consent to one or more data services. The processor(s) is also programmed to receive transaction card details from the cardholder computing device. The transaction card details correspond to a transaction card of a cardholder and are associated with a financial account of the cardholder. Based on the transaction card details, the processor(s) generates a digital access token. The digital access token is associated with the financial account and the cardholder consent information. The processor(s) stores the digital access token in the database, transmits a cardholder authentication request message to an issuer, and receives an authentication ID from the cardholder computing device.

Abstract: Identifying and resolving weaknesses in software are common, resource-intensive tasks for many organizations. Machine-learning models are provided to automatically identify software vulnerabilities or other flaws, such as via entries in a weakness or vulnerability database, identify affected software, generate patches to resolve the vulnerabilities, and apply the patch to affected software. The patch is automatically extracted from code deltas between a software version having the weakness and a subsequent version wherein the weakness has been resolved. Other differences between the versions, not affecting the weakness, are excluded from the code deltas.

Abstract: A computer system includes a database, a communication interface, and a processor(s). The processor(s) is programmed to receive a consent message from a cardholder computing device. The consent message includes cardholder consent information indicating consent to one or more data services. The processor(s) is also programmed to decrypt transaction card details included in a secure request message. The transaction card details correspond to a transaction card of a cardholder and are associated with a financial account of the cardholder. Based on the transaction card details, the processor(s) generates a digital access token. The digital access token is associated with the financial account and the cardholder consent information. The processor(s) stores the digital access token in the database, transmits a cardholder authentication request message to an issuer, and receives an authentication ID from the cardholder computing device.

Abstract: A computer system includes a database, a communication interface, and a processor. The processor is programmed to receive a request message from an issuer computing device. The consent message includes a client ID and encrypted transaction card details for a transaction card account. The processor is also programmed to decrypt transaction card details. The processor is also programmed to match the client ID to the transaction card details using a BIN mapping table stored in the database. Based on the mapping, the processor generates an issuer access token. The issuer access token is associated with the transaction card account. The processor stores the issuer access token in the database and transmits the token to the issuer computing device.

Abstract: A computer system includes a database, a communication interface, and a processor(s). The processor(s) is programmed to receive a consent message from a cardholder computing device. The consent message includes cardholder consent information indicating consent to one or more data services. The processor(s) is also programmed to receive transaction card details from the cardholder computing device. The transaction card details correspond to a transaction card of a cardholder and are associated with a financial account of the cardholder. Based on the transaction card details, the processor(s) generates a digital access token. The digital access token is associated with the financial account and the cardholder consent information. The processor(s) stores the digital access token in the database, transmits a cardholder authentication request message to an issuer, and receives an authentication ID from the cardholder computing device.

Abstract: In some examples, a method may include detecting a vulnerability in an application during execution on a first computing device. The method may include triggering a breakpoint based on the detecting, thereby pausing the execution of the application before execution of a portion of code that exploits the vulnerability. The method may include communicating a message indicating occurrence of the breakpoint. The method may include receiving a connection request from a second computing device in response to the message. The method may include resuming execution of the application from the breakpoint subject to a signal from the second computing device.

Abstract: A technique includes receiving data representing issues identified in a security scan of an application and features associated with the issues. The technique includes processing the data in a processor-based machine to selectively assign classifiers to the security issues based at least in part on the features. The technique includes using the assigned classifiers to classify the issues.