A Large Language Model-Supported Threat Modeling Framework for Transportation Cyber-Physical Systems

TL;DR

This paper introduces TraCR-TMF, an LLM-based threat modeling framework for transportation cyber-physical systems that reduces expert involvement while effectively identifying threats, attack paths, and mitigations.

Contribution

The paper presents a novel LLM-supported framework for transportation CPS threat modeling, enabling automated threat identification and attack path prediction with minimal cybersecurity expertise.

Findings

73% of identified attack techniques validated by experts

Successfully predicted attack paths in a real-world incident

Reduced cybersecurity expert involvement in threat modeling

Abstract

Existing threat modeling frameworks related to transportation cyber-physical systems (CPS) are often narrow in scope, labor-intensive, and require substantial cybersecurity expertise. To this end, we introduce the Transportation Cybersecurity and Resiliency Threat Modeling Framework (TraCR-TMF), a large language model (LLM)-based threat modeling framework for transportation CPS that requires limited cybersecurity expert intervention. TraCR-TMF identifies threats, potential attack techniques, and relevant countermeasures for transportation CPS. Three LLM-based approaches support these identifications: (i) a retrieval-augmented generation approach requiring no cybersecurity expert intervention, (ii) an in-context learning approach with low expert intervention, and (iii) a supervised fine-tuning approach with moderate expert intervention. TraCR-TMF offers LLM-based attack path identification for critical assets based on vulnerabilities across transportation CPS entities. Additionally, it incorporates the Common Vulnerability Scoring System (CVSS) scores of known exploited vulnerabilities to prioritize threat mitigations. The framework was evaluated through two cases. First, the framework identified relevant attack techniques for various transportation CPS applications, 73% of which were validated by cybersecurity experts as correct. Second, the framework was used to identify attack paths for a target asset in a real-world cyberattack incident. TraCR-TMF successfully predicted exploitations, like lateral movement of adversaries, data exfiltration, and data encryption for ransomware, as reported in the incident. These findings show the efficacy of TraCR-TMF in transportation CPS threat modeling, while reducing the need for extensive involvement of cybersecurity experts. To facilitate real-world adoptions, all our codes are shared via an open-source repository.