Abstract: In some examples, scalable source code vulnerability remediation may include receiving source code that includes at least one vulnerability, and receiving remediated code that remediates the at least one vulnerability associated with the source code. At least one machine learning model may be trained to analyze a vulnerable code snippet of the source code. The vulnerable code snippet may correspond to the at least one vulnerability associated with the source code. The machine learning model may be trained to generate, for the vulnerable code snippet, a remediated code snippet to remediate the at least one vulnerability associated with the source code. The remediated code snippet may be validated based on an analysis of whether the remediated code snippet remediates the at least one vulnerability associated with the source code.

Abstract: A code remediation system accesses a programming code including vulnerabilities such as potential secrets and remediates at least a subset of the potential secrets to generate modified programming code wherein the subset of potential secrets which are determined to be actual secrets are replaced with access mechanisms to storage locations on a vault wherein the actual secrets are secured. To identify the subset of potential secrets forming the actual secrets to be remediated, the code remediation system is configured to filter out false positives among the potential secrets and identify true positives. When an application executing the modified code encounters an access mechanism, it accesses the vault to retrieve the actual secrets.

Abstract: Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their triage based on automated decision-tree triage and/or machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and automatically determine whether the potential vulnerability issues are exploitable using automated triage policies containing decision trees or by extracting vulnerability features from the report and processing the extracted vulnerability features using machine learning models.

Abstract: Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their triage based on automated decision-tree triage and/or machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and automatically determine whether the potential vulnerability issues are exploitable using automated triage policies containing decision trees or by extracting vulnerability features from the report and processing the extracted vulnerability features using machine learning models.

Abstract: A device may receive contextual data, computational data, experiential data, and industry data associated with software code, and may receive detected vulnerabilities data identified by a scanning model based on the software code and software code metadata of the computational data. The device may process the contextual data, the computational data, and the experiential data, with a contextual identification model, to determine a set of rules and a set of actions, and may enrich, via an enrichment model, the industry data with the experiential data to generate enriched industry data. The device may process the software code metadata, the detected vulnerabilities data, and the enriched industry data, with a correlation model, to generate analysis data, and may process the analysis data and the set of rules, with a security model, to confirm security issues associated with the software code. The device may perform one of the set of actions based on the security issues.