Automated Threat Modeling with Large Language Models
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Solution Overview
Problem
Traditional threat modeling methods are time-consuming, resource-intensive, and prone to human errors and biases, leading to outdated assessments and reactive security measures that fail to adapt to rapidly evolving threats.
Innovation Solution
A system for continuous automated threat modeling using large language models, which ingests application profiles, workload contexts, and software templates to generate threat models, prompts, and reports, enabling continuous security assessments and mitigation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual threat modeling processes are used, then security assessments can be performed, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical threat modeling processes with an automated system using large language models. The LLM-based engine automatically generates threat models, identifies vulnerabilities, and produces security assessments without human intervention in the actual modeling process, thereby eliminating time consumption while maintaining assessment quality through systematic automated analysis
Solution Approach 2:
The system enables self-service threat modeling where the automated LLM-based engine independently performs security assessments without requiring human experts to manually execute each step. The system serves itself by automatically ingesting application profiles, generating threat models, and producing reports, freeing human resources while maintaining continuous security evaluation
2Reliability
If traditional one-off threat modeling activities are performed during design phase, then initial security assessments are obtained, but the assessments become outdated and fail to adapt to evolving threats
Solution Approach 1:
The patent implements continuous threat modeling where the LLM-based engine operates continuously rather than as one-off activities. The system continuously ingests updated application profiles, re-generates threat models, and produces ongoing security assessments, ensuring threat assessments remain current and adapt to evolving threats through persistent automated analysis
Solution Approach 2:
The system transitions from static periodic assessments to dynamic continuous assessment. The LLM-based engine adapts to changing threats by continuously processing new information, updating threat models in real-time, and generating current security assessments that reflect the latest threat landscape and application changes
3Reliability
If manual threat modeling processes are used, then human expertise can be applied, but human biases and mistakes affect the assessment quality
Solution Approach 1:
The patent replaces human manual processes with an LLM-based automated system that eliminates human biases and mistakes from threat modeling. The systematic automated analysis provides objective assessments free from human error, fatigue, or cognitive biases, while the LLM's trained knowledge base ensures consistent application of security principles without subjective variation
Data Source
AI summary
A system and method for continuous automated threat modeling which is based on prompt engineering using large language models includes a threat modeling engine, a threat prompt generator, and a continuous automation module configured to retrieve a threat prompt from the threat prompt generator and to perform a security assessment of the threat prompt on a continuous, automatic basis. In addition, the system and method each include integration with a large language model for generating threat models and mitigations pertaining to those threat models.