Risk Assessment and Threat Modeling for safe autonomous driving technology

TL;DR

This paper addresses the cybersecurity risk of autonomous driving systems by formalizing a comprehensive threat model and risk assessment framework. It adopts STRIDE-based threat modeling with OWASP Threat Dragon and DREAD scoring to analyze AV components—from sensing and perception to planning, decision-making, and control—and their external interfaces. The authors identify 42 threats, provide mappings, security mitigations, and alignments to AUTOSAR, OWASP Top Ten, and ISO/SAE 21434, including modeling environmental conditions as non-malicious threats. The results offer a structured, defense-in-depth blueprint for secure AV deployment and risk-informed design decisions.

Abstract

This research paper delves into the field of autonomous vehicle technology, examining the vulnerabilities inherent in each component of these transformative vehicles. Autonomous vehicles (AVs) are revolutionizing transportation by seamlessly integrating advanced functionalities such as sensing, perception, planning, decision-making, and control. However, their reliance on interconnected systems and external communication interfaces renders them susceptible to cybersecurity threats. This research endeavors to develop a comprehensive threat model for AV systems, employing OWASP Threat Dragon and the STRIDE framework. This model categorizes threats into Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service (DoS), and Elevation of Privilege. A systematic risk assessment is conducted to evaluate vulnerabilities across various AV components, including perception modules, planning systems, control units, and communication interfaces.

Figures (2)

• Figure 1: Autonomous vehicles components
• Figure 2: Autonomous Vehicles Threat Model