ThreatModeling-LLM: Automating Threat Modeling using Large Language Models for Banking System

TL;DR

This paper presents ThreatModeling-LLM, an innovative framework that automates threat modeling for banking systems by leveraging large language models, addressing domain-specific challenges, and enhancing efficiency and accuracy.

Contribution

It introduces a comprehensive, adaptable approach combining dataset creation, prompt engineering, and model fine-tuning to automate threat modeling in banking environments.

Findings

Generated a benchmark dataset using Microsoft Threat Modeling Tool

Optimized prompts with Chain of Thought and OPRO techniques

Fine-tuned LLMs with LoRA to improve threat detection and mitigation

Abstract

Threat modeling is a crucial component of cybersecurity, particularly for industries such as banking, where the security of financial data is paramount. Traditional threat modeling approaches require expert intervention and manual effort, often leading to inefficiencies and human error. The advent of Large Language Models (LLMs) offers a promising avenue for automating these processes, enhancing both efficiency and efficacy. However, this transition is not straightforward due to three main challenges: (1) the lack of publicly available, domain-specific datasets, (2) the need for tailored models to handle complex banking system architectures, and (3) the requirement for real-time, adaptive mitigation strategies that align with compliance standards like NIST 800-53. In this paper, we introduce ThreatModeling-LLM, a novel and adaptable framework that automates threat modeling for banking systems using LLMs. ThreatModeling-LLM operates in three stages: 1) dataset creation, 2) prompt engineering and 3) model fine-tuning. We first generate a benchmark dataset using Microsoft Threat Modeling Tool (TMT). Then, we apply Chain of Thought (CoT) and Optimization by PROmpting (OPRO) on the pre-trained LLMs to optimize the initial prompt. Lastly, we fine-tune the LLM using Low-Rank Adaptation (LoRA) based on the benchmark dataset and the optimized prompt to improve the threat identification and mitigation generation capabilities of pre-trained LLMs.