LLM Threat Modeling for Secure Code in Software Development
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Solution Overview
Problem
Existing software development processes often overlook security considerations during the design phase, leading to time-intensive and less effective incorporation of security requirements post-development, which can result in suboptimal application security.
Innovation Solution
Integrate a trained large language model (LLM) into the software development platform to generate secure code and assist with threat modeling tasks, using a retrieval-augmented generation protocol to reduce hallucinations and ensure security requirements are met during the design phase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security requirements are incorporated after development, then security vulnerabilities can be addressed, but development time increases and security effectiveness decreases
Solution Approach 1:
The patent applies preliminary action by integrating security requirements into the design phase before development occurs. The threat modeling process identifies security vulnerabilities and defines security requirements upfront, which then guide the coding process. This ensures security is built-in rather than added later, reducing overall development time while improving security effectiveness.
Solution Approach 2:
The patent implements feedback mechanisms through automated security testing and code review processes that provide continuous feedback during development. Security requirements are validated against actual code implementation, allowing for iterative improvements and ensuring security effectiveness without significant time penalties.
2Reliability
If traditional threat modeling tools are used, then security vulnerabilities can be identified, but the process is time-intensive and less effective
Solution Approach 1:
The patent replaces traditional mechanical threat modeling tools with an AI-powered system that uses large language models to automatically perform threat modeling. The system analyzes code, generates threat models, and identifies vulnerabilities automatically, significantly improving productivity while maintaining or enhancing security vulnerability identification capabilities.
Solution Approach 2:
The patent implements self-service through automated security analysis where the system independently performs threat modeling, code review, and vulnerability identification without requiring extensive manual intervention. The AI system autonomously processes security requirements and generates appropriate security code, improving efficiency while maintaining security reliability.
3Reliability
If security features are added post-development, then existing vulnerabilities can be fixed, but the application design becomes less effective
Solution Approach 1:
The patent applies preliminary action by establishing security requirements and threat models during the design phase. This allows security considerations to shape the application architecture from the beginning, ensuring the design remains effective and adaptable rather than being compromised by post-development security additions.
Solution Approach 2:
The patent merges security requirements with functional requirements during the design phase. By combining these requirement types, the system ensures that security considerations are integrated into the overall application design, maintaining design effectiveness while addressing security vulnerabilities simultaneously.
Data Source
AI summary
The present disclosure of the various embodiments relates to using a large language model to assistant with the creation of secure code and/or the completion of threat modeling tasks in software development. In one example, a system comprises a computing device configure to identify a prompt that requests generating secure source code for source code with a security vulnerability. A security data source is queried for a security threat embedding. The security threat embedding is received from the security data source and an augmented prompt is generated. The augmented prompt is transmitted to the large language model. A secure source code is received from the large language model and imported into application source code in a software development environment.