Structure-Aware, Diagnosis-Guided ECU Firmware Fuzzing

Electronic Control Units (ECUs), providing a wide range of functions from basic control functions to safety-critical functions, play a critical role in modern vehicles. Fuzzing has emerged as an effective approach to ensure the functional safety and automotive security of ECU firmware. However, existing fuzzing approaches focus on the inputs from other ECUs through external buses (e.g., CAN), but neglect the inputs from internal peripherals through on-board buses (e.g., SPI). Due to the restricted input space exploration, they fail to comprehensively fuzz ECU firmware. Moreover, existing fuzzing approaches often lack visibility into ECU firmware’ internal states but rely on limited feedback (e.g., message timeouts or hardware indicators), hindering their effectiveness. To address these limitations, we propose a structure-aware, diagnosis-guided framework, EcuFuzz, to comprehensively and effectively fuzz ECU firmware. Specifically, EcuFuzzsimultaneously considers external buses (i.e., CAN) and on-board buses (i.e., SPI). It leverages the structure of CAN and SPI to effectively mutate CAN messages and SPI sequences, and incorporates a dual-core microcontroller-based peripheral emulator to handle real-time SPI communication. In addition, EcuFuzzimplements a new feedback mechanism to guide the fuzzing process. It leverages automotive diagnostic protocols to collect ECUs’ internal states, i.e., error-related variables, trouble codes, and exception contexts. Our compatibility evaluation on ten ECUs from three major Tier 1 automotive suppliers has indicated that our framework is compatible with nine ECUs. Our effectiveness evaluation on three representative ECUs has demonstrated that our framework detects nine previously unknown safety-critical faults, which have been patched by technicians from the suppliers.

Authors

Institutions

Publication Details

Journal
Proceedings of the ACM on software engineering.
Published
2025-06-22
DOI
https://doi.org/10.1145/3728914
Citations
2
Primary Topic
Real-time simulation and control systems
Type
article
Field-Weighted Citation Impact
2.43
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Structure-Aware, Diagnosis-Guided ECU Firmware Fuzzing

Qing‐Quan Chen, Bingkun Sun, Bihuan Chen, Kun Hu et al.
2 citations
Proceedings of the ACM on software engineering.
Real-time simulation and control systems
2.43
article

Structure-Aware, Diagnosis-Guided ECU Firmware Fuzzing

Qing‐Quan Chen, Bingkun Sun, Bihuan Chen, Kun Hu, Xin Peng, You Jun Lu, Haowen Jiang, Shi-Hao Gong, Zikui Kong
article en
2 citations

Abstract

Electronic Control Units (ECUs), providing a wide range of functions from basic control functions to safety-critical functions, play a critical role in modern vehicles. Fuzzing has emerged as an effective approach to ensure the functional safety and automotive security of ECU firmware. However, existing fuzzing approaches focus on the inputs from other ECUs through external buses (e.g., CAN), but neglect the inputs from internal peripherals through on-board buses (e.g., SPI). Due to the restricted input space exploration, they fail to comprehensively fuzz ECU firmware. Moreover, existing fuzzing approaches often lack visibility into ECU firmware’ internal states but rely on limited feedback (e.g., message timeouts or hardware indicators), hindering their effectiveness. To address these limitations, we propose a structure-aware, diagnosis-guided framework, EcuFuzz, to comprehensively and effectively fuzz ECU firmware. Specifically, EcuFuzzsimultaneously considers external buses (i.e., CAN) and on-board buses (i.e., SPI). It leverages the structure of CAN and SPI to effectively mutate CAN messages and SPI sequences, and incorporates a dual-core microcontroller-based peripheral emulator to handle real-time SPI communication. In addition, EcuFuzzimplements a new feedback mechanism to guide the fuzzing process. It leverages automotive diagnostic protocols to collect ECUs’ internal states, i.e., error-related variables, trouble codes, and exception contexts. Our compatibility evaluation on ten ECUs from three major Tier 1 automotive suppliers has indicated that our framework is compatible with nine ECUs. Our effectiveness evaluation on three representative ECUs has demonstrated that our framework detects nine previously unknown safety-critical faults, which have been patched by technicians from the suppliers.

Proceedings of the ACM on software engineering.Vol. 2(ISSTA)
Fudan University (CN)
Openalex Percentile: Top 11%
Real-time simulation and control systems
2.43
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.