Systems and methods for detecting and remediating security vulnerabilities in source code using machine learning

A computing system, comprising:

• a processor;
• and
• a memory having programming instructions stored thereon, which, when executed by the processor, cause the computing system to perform operations comprising:
• receiving, using a generative machine learning model of a first computing system, first code and information associated with the first code, the information representing an impact analysis, system documentation, and a plurality of requirements;
• tokenizing, using the generative machine learning model, the first code and the information associated with the first code;
• determining, using the generative machine learning model, a plurality of characteristics associated with the first code based on the tokenized first code and the tokenized information associated with the first code;
• determining, using the generative machine learning model, a plurality of security vulnerabilities associated with the first code based on the plurality of characteristics;
• ranking, using the generative machine learning model, each of the plurality of security vulnerabilities associated with the first code based at least in part on the impact analysis;
• and
• determining, using the generative machine learning model, at least second code to remediate one or more of the plurality of security vulnerabilities.

A method comprising:

• receiving, using a large language model (LLM), first code and information associated with the first code, the information representing an impact analysis, system documentation, and a plurality of requirements;
• tokenizing, using the LLM, the first code and the information associated with the first code;
• determining, using the LLM, a plurality of characteristics associated with the first code based on the tokenized first code and the tokenized information associated with the first code;
• determining, using the LLM, a plurality of security vulnerabilities associated with the first code based on the plurality of characteristics;
• ranking, using the LLM, each of the plurality of security vulnerabilities associated with the first code based on the impact analysis and a likelihood of a respective one of the plurality of security vulnerabilities being exploited;
• and
• determining, using the LLM, at least second code to remediate one or more of the plurality of security vulnerabilities.