Abstract: A method of leveraging large language models (LLMs) to produce a deterministic analysis of data is disclosed. The system receives prepared data and a series of prompts that are used to analyze the prepared data. The system iteratively uses the prepared data and a prompt from the series of prompts as input to the LLMs. Each prompt can be designed to result in responses of a specific format or type, thereby reducing random variability in the output of the LLMs after each prompt. The final output of the system is deterministic, depending only on the prepared data and the series of prompts. Furthermore, performing the analysis in steps allows for quickly modifying and optimizing the process.

Abstract: A method of leveraging large language models (LLMs) to produce a deterministic analysis of data is disclosed. The system receives prepared data and a series of prompts that are used to analyze the prepared data. The system iteratively uses the prepared data and a prompt from the series of prompts as input to the LLMs. Each prompt can be designed to result in responses of a specific format or type, thereby reducing random variability in the output of the LLMs after each prompt. The final output of the system is deterministic, depending only on the prepared data and the series of prompts. Furthermore, performing the analysis in steps allows for quickly modifying and optimizing the process.

Abstract: The disclosed technology pertains to the detection of security vulnerabilities in proposed changes to a codebase. Upon receiving a proposed change, the system determines a code context based on the data and metadata of the changed files. The proposed change is then divided into code segments processed by multiple code analyzers in parallel, each focusing on specific security concerns. The system can utilize large language models (LLMs) to enhance the analysis, providing more accurate and comprehensive detection of vulnerabilities. Furthermore, the system can present to a user a user interface comprising security information and answers to one or more natural-language security questions.

Abstract: The disclosed technology pertains to the detection of security vulnerabilities in proposed changes to a codebase. Upon receiving a proposed change, the system determines a code context based on the data and metadata of the changed files. The proposed change is then divided into code segments processed by multiple code analyzers in parallel, each focusing on specific security concerns. The system can utilize large language models (LLMs) to enhance the analysis, providing more accurate and comprehensive detection of vulnerabilities. Furthermore, the system can present to a user a user interface comprising security information and answers to one or more natural-language security questions.

Abstract: Context-aware authorization verification for Application Programming Interface (API) endpoints using large language models (LLMs) is disclosed herein. A system receives source code including an implementation file and at least one service definition file. The service definition file(s) include metadata and a service interface definition. The system determines that the source code implements an API handler by determining that the implementation file includes a reference to the service interface definition. The metadata is used to determine that the API handler is associated with a public endpoint. An LLM is used to determine that the source code lacks an authorization check by determining absence of a direct call to an authorization function, and determining absence of an indirect authorization check. An alert is generated indicating that the source code implements an API handler associated with a public endpoint and that the source code lacks an authorization check.

Abstract: The disclosed technology pertains to the detection of security vulnerabilities in proposed changes to a codebase. Upon receiving a proposed change, the system determines a code context based on the data and metadata of the changed files. The proposed change is then divided into code segments processed by multiple code analyzers in parallel, each focusing on specific security concerns. The system can utilize large language models (LLMs) to enhance the analysis, providing more accurate and comprehensive detection of vulnerabilities. Furthermore, the system can present to a user a user interface comprising security information and answers to one or more natural-language security questions.