Automating Threat Modeling in Banking with LLMs
/ 4 min read
Quick take - The article discusses the development of ThreatModeling-LLM, a framework that utilizes Large Language Models to automate threat modeling in the banking sector, addressing challenges such as the need for domain-specific datasets and customized models while demonstrating improved performance in threat identification and mitigation generation.
Fast Facts
- Threat modeling is crucial in cybersecurity for the banking sector, but traditional methods are often inefficient and prone to human error.
- Large Language Models (LLMs) offer a promising solution for automating threat modeling, though challenges include the need for domain-specific datasets and customized models.
- The ThreatModeling-LLM framework automates threat modeling in banking systems through three stages: dataset creation, prompt engineering, and model fine-tuning.
- A benchmark dataset was developed using the Microsoft Threat Modeling Tool, enhancing the model’s ability to identify threats and generate mitigations, with performance improvements noted in experimental results.
- The study emphasizes the importance of real-time mitigation strategies and the integration of established standards like NIST 800-53 to ensure effective threat modeling in complex banking environments.
Threat Modeling in Cybersecurity
Threat modeling is a vital aspect of cybersecurity, especially in the banking sector, where protecting financial data is crucial. Traditional threat modeling methods often involve expert input and manual processes, which can lead to inefficiencies and a higher risk of human error. Recently, Large Language Models (LLMs) have emerged as potential tools to automate threat modeling, enhancing both efficiency and effectiveness.
Challenges in Adopting LLMs
A major challenge in adopting LLMs for threat modeling is the lack of publicly available, domain-specific datasets. There is also a need for customized models that can address the complexities of banking system architectures. Real-time, adaptive mitigation strategies that comply with established standards, such as NIST 800-53, are essential for successful implementation.
In response to these challenges, researchers have developed ThreatModeling-LLM, a framework designed to automate threat modeling for banking systems using LLMs. This framework operates in three stages: dataset creation, prompt engineering, and model fine-tuning.
Framework Development and Methodology
A benchmark dataset is created using the Microsoft Threat Modeling Tool (TMT), which includes Data Flow Diagrams (DFDs) and human-annotated mitigation strategies aligned with NIST 800-53. The approach incorporates advanced techniques like Chain of Thought (CoT) and Optimization by PROmpting (OPRO) to refine prompts for pre-trained LLMs. The model is then fine-tuned using Low-Rank Adaptation (LoRA) based on the benchmark dataset and optimized prompts, enhancing its threat identification and mitigation generation capabilities.
Experimental results show significant improvements in the model’s performance, with mitigation code accuracy increasing from 0.36 to 0.69 on the Llama-3.1-8B-Instruct model. This innovative combination of prompt engineering and model fine-tuning is effective for automated threat modeling, positioning ThreatModeling-LLM as a promising solution for banking systems and potentially other applications.
Future Directions and Considerations
Traditional modeling techniques often rely heavily on manual input and the creation of DFDs, underscoring the need for more efficient, automated solutions amid the complex confidentiality, integrity, and privacy requirements in banking. LLMs can analyze textual descriptions of system designs, enabling automatic threat identification and corresponding mitigations, which could significantly improve accuracy while reducing manual intervention.
However, existing tools like STRIDEGPT and Cyber Sentinel highlight the ongoing trade-offs between automation and precision in threat modeling processes. Adapting LLMs for this purpose presents several challenges, including the need for publicly accessible datasets, tailored models specific to banking systems, and effective real-time mitigation strategies.
To address the dataset gap, the research team developed a benchmark dataset covering 50 different banking system applications and use cases, ensuring relevance and accuracy through collaboration with local banking experts. The dataset creation process is complemented by prompt engineering, where various templates are explored to maximize the LLM’s output for threat and mitigation identification.
The study also reviews related works in threat modeling, outlining both strengths and limitations, particularly concerning automation and the need for domain-specific adaptation. The STRIDE framework categorizes security threats into six types: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege, providing a foundational structure for understanding and managing cybersecurity risks.
Meanwhile, the NIST Cybersecurity Framework and NIST 800-53 offer comprehensive guidance on managing cybersecurity risks and implementing detailed security and privacy controls. The research aims to revolutionize the threat modeling process for banking systems by using LLMs to transform textual descriptions into identified threats and corresponding mitigation strategies.
The proposed system consists of three key components: Data Creation, Prompt Engineering, and Model Fine-tuning. The fine-tuning phase focuses on efficiently adapting pre-trained models to domain-specific language patterns and structures, employing critical parameters such as rank, scaling factor, target modules, and dropout rate to mitigate overfitting. The training configuration includes essential elements like batch size, optimizer selection, learning rate, and the number of epochs to ensure effective model adaptation.
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