Revisiting Automotive Attack Surfaces: a Practitioners' Perspective

As modern vehicles become increasingly complex in terms of both external attack surfaces and internal in-vehicle network (IVN) topology, ensuring their cybersecurity remains a challenge. Existing standards and regulations, such as WP29 R155e and ISO 21434, attempt to establish a baseline for automotive cybersecurity, but their sufficiency in addressing the evolving threats is unclear. To fill in this gap, we first carried out an in-depth interview study with 15 experts in automotive cybersecurity, uncovering the particular challenges encountered during security activities and the limitations of current regulations. We identified 20 key insights from the interview data, ranging from the challenges and gaps in the existing automotive security industry to the limitations and recommendations for current regulations. Notably, we discovered that the quality of threat cases provided by existing regulations is unsatisfactory, and the Threat Analysis and Risk Assessment (TARA) process is often highly inefficient due to the lack of automatic tools. In response to the above limitations, we first built an improved threat database for automotive systems using the collected interview data, which enhanced the existing database both quantitatively and qualitatively. Additionally, we present CarVal, a datalog-based approach designed to infer multi-stage attack paths in IVNs and calculate risk values, thereby making TARA more efficient for automotive systems. By applying CarVal to five real vehicles, we performed extensive security analysis based on the generated attack paths and successfully exploited the corresponding attack chains in the newly gateway-segmented IVN, uncovering new automotive attack surfaces that previous research failed to cover, including the in-vehicle browser, official mobile app, backend server, and in-vehicle malware.