Audio-Visual Multimodal LLM Threat Modeling for Application Security
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Threat modeling for complex application architectures is challenging and time-consuming, often leading to developer avoidance or incomplete implementation, which hinders secure application development.
Innovation Solution
A multimodal large language model (LLM)-based threat modeling system that uses audio and visual prompts to simplify the threat modeling process, incorporating interleaved language and visual modalities to automate the generation of threat models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual threat modeling is performed to ensure comprehensive security analysis, then security quality is improved, but time consumption and resource usage increase significantly
Solution Approach 1:
The patent introduces an AI-based threat modeling system as an intermediary between developers and security analysis. This automated system processes application architecture descriptions and generates comprehensive threat models, security requirements, and mitigation strategies without requiring developer time investment, thus maintaining high security quality while eliminating time consumption.
Solution Approach 2:
The threat modeling system performs self-service by automatically analyzing application architectures and generating security documentation without human intervention. The system independently identifies threats, evaluates risks, and produces security requirements, freeing developers from manual security analysis tasks while ensuring comprehensive security coverage.
2Reliability
If manual threat modeling is performed to identify and document potential security threats, then security decision-making is improved, but developer productivity decreases
Solution Approach 1:
The patent replaces the mechanical manual process of threat modeling with an automated AI system. Instead of developers manually analyzing architectures and documenting threats, the system automatically processes architecture descriptions and generates comprehensive security analyses, maintaining high-quality security decision-making while completely eliminating the productivity loss associated with manual processes.
3Reliability
If comprehensive threat modeling is performed to analyze complex application architectures, then security coverage is improved, but process complexity increases
Solution Approach 1:
The patent segments the complex threat modeling process into distinct automated stages: architecture description input, automated threat identification, risk evaluation, and security requirement generation. Each stage is handled independently by the AI system, maintaining comprehensive security coverage while simplifying the overall process through systematic breakdown of complex analysis tasks.
Data Source
AI summary
Disclosed are various approaches for multimodal large language model (LLM) based threat modeling. The multimodal LLM based threat modeling can include a system or method that can input, into a threat modeling multimodal LLM, prompting data that includes audio data, image data, and LLM instructions to generate application security data. The threat modeling multimodal LLM can generate and provide application security data that includes at least one of: threat data, weakness data, security control data, a security risk summarization, an application threat model, or any combination thereof.