Fuzzing: a survey for roadmap

X Zhu, S Wen, S Camtepe, Y Xiang - ACM Computing Surveys (CSUR), 2022 - dl.acm.org
Fuzz testing (fuzzing) has witnessed its prosperity in detecting security flaws recently. It
generates a large number of test cases and monitors the executions for defects. Fuzzing has …

Fuzzing of embedded systems: A survey

J Yun, F Rustamov, J Kim, Y Shin - ACM Computing Surveys, 2022 - dl.acm.org
Security attacks abuse software vulnerabilities of IoT devices; hence, detecting and
eliminating these vulnerabilities immediately are crucial. Fuzzing is an efficient method to …

Snapfuzz: high-throughput fuzzing of network applications

A Andronidis, C Cadar - Proceedings of the 31st ACM SIGSOFT …, 2022 - dl.acm.org
In recent years, fuzz testing has benefited from increased computational power and
important algorithmic advances, leading to systems that have discovered many critical bugs …

Too afraid to drive: systematic discovery of semantic dos vulnerability in autonomous driving planning under physical-world attacks

Z Wan, J Shen, J Chuang, X Xia, J Garcia, J Ma… - arXiv preprint arXiv …, 2022 - arxiv.org
In high-level Autonomous Driving (AD) systems, behavioral planning is in charge of making
high-level driving decisions such as cruising and stopping, and thus highly securitycritical. In …

On the (in) security of secure ros2

G Deng, G Xu, Y Zhou, T Zhang, Y Liu - Proceedings of the 2022 ACM …, 2022 - dl.acm.org
Robot Operating System (ROS) has been the mainstream platform for research and
development of robotic applications. This platform is well-known for lacking security features …

Linear-time temporal logic guided greybox fuzzing

R Meng, Z Dong, J Li, I Beschastnikh… - Proceedings of the 44th …, 2022 - dl.acm.org
Software model checking as well as runtime verification are verification techniques which
are widely used for checking temporal properties of software systems. Even though they are …

Discovering adversarial driving maneuvers against autonomous vehicles

R Song, MO Ozmen, H Kim, R Muller, ZB Celik… - 32nd USENIX Security …, 2023 - usenix.org
Over 33% of vehicles sold in 2021 had integrated autonomous driving (AD) systems. While
many adversarial machine learning attacks have been studied against these systems, they …

{PatchVerif}: Discovering Faulty Patches in Robotic Vehicles

H Kim, MO Ozmen, ZB Celik, A Bianchi… - 32nd USENIX Security …, 2023 - usenix.org
Modern software is continuously patched to fix bugs and security vulnerabilities. Patching is
particularly important in robotic vehicles (RVs), in which safety and security bugs can cause …

SoK: Rethinking sensor spoofing attacks against robotic vehicles from a systematic view

Y Xu, X Han, G Deng, J Li, Y Liu… - 2023 IEEE 8th European …, 2023 - ieeexplore.ieee.org
Robotic Vehicles (RVs) have gained great popularity over the past few years. Meanwhile,
they are also demonstrated to be vulnerable to sensor spoofing attacks. Although a wealth of …

RoboFuzz: fuzzing robotic systems over robot operating system (ROS) for finding correctness bugs

S Kim, T Kim - Proceedings of the 30th ACM Joint European Software …, 2022 - dl.acm.org
Robotic systems are becoming an integral part of human lives. Responding to the increased
demands for robot productions, Robot Operating System (ROS), an open-source …