UDS Attack Taxonomy: Systematic Classification of Vehicle Diagnostic Threats

The increasing complexity and connectivity of mod-ern vehicles have introduced significant security challenges. This also applies to the Unified Diagnostic Services (UDS) protocol, a communication standard used in the automotive industry for diagnostic and maintenance purposes. This paper presents a comprehensive analysis of potential attack techniques targeting the UDS protocol. Leveraging the automotive-specific VATT&EK framework (a vehicle-specialized adaptation of the MITRE ATT&CK methodology), we systematically identify and categorize adversarial techniques for exploiting UDS vulnerabilities. Our analysis demonstrates the practical applicability of the resulting taxonomy by mapping it to existing attack scenarios from pub-lished literature, showing concrete examples for nearly two-thirds of the identified techniques. The presented taxonomy provides a structured approach for security assessments, incident response, and the development of security monitoring strategies for UDS, thus addressing critical cybersecurity challenges in modern connected vehicles. The work offers enhanced coverage beyond the commonly focused Security Access service, providing a more holistic understanding of poten-tial end-to-end attack scenarios and showcasing the adaptability of the VATT&EK (and in a broader sense also the MITRE ATT &CK) framework to the automotive domain.

Authors

Institutions

Publication Details

Published
2025-09-08
DOI
https://doi.org/10.1109/cns66487.2025.11195020
Primary Topic
Web Application Security Vulnerabilities
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

UDS Attack Taxonomy: Systematic Classification of Vehicle Diagnostic Threats

Jens Gramm, Stefan Katzenbeisser, Nicolas Loza, Michael Schneider et al.
Web Application Security Vulnerabilities
article

UDS Attack Taxonomy: Systematic Classification of Vehicle Diagnostic Threats

Jens Gramm, Stefan Katzenbeisser, Nicolas Loza, Michael Schneider, Ali Recai Yekta
article en

Abstract

The increasing complexity and connectivity of mod-ern vehicles have introduced significant security challenges. This also applies to the Unified Diagnostic Services (UDS) protocol, a communication standard used in the automotive industry for diagnostic and maintenance purposes. This paper presents a comprehensive analysis of potential attack techniques targeting the UDS protocol. Leveraging the automotive-specific VATT&EK framework (a vehicle-specialized adaptation of the MITRE ATT&CK methodology), we systematically identify and categorize adversarial techniques for exploiting UDS vulnerabilities. Our analysis demonstrates the practical applicability of the resulting taxonomy by mapping it to existing attack scenarios from pub-lished literature, showing concrete examples for nearly two-thirds of the identified techniques. The presented taxonomy provides a structured approach for security assessments, incident response, and the development of security monitoring strategies for UDS, thus addressing critical cybersecurity challenges in modern connected vehicles. The work offers enhanced coverage beyond the commonly focused Security Access service, providing a more holistic understanding of poten-tial end-to-end attack scenarios and showcasing the adaptability of the VATT&EK (and in a broader sense also the MITRE ATT &CK) framework to the automotive domain.

Dortmund University of Applied Sciences and Arts (DE), University of Passau (DE), Etas (Germany) (DE)
Openalex Percentile: Top 57%
Web Application Security Vulnerabilities
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.