Ruotong Yu (Stevens Institute of Technology, University of Utah), Francesca Del Nin (University of Padua), Yuchen Zhang (Stevens Institute of Technology), Shan Huang (Stevens Institute of Technology), Pallavi Kaliyar (Norwegian University of Science and Technology), Sarah Zakto (Cyber Independent Testing Lab), Mauro Conti (University of Padua, Delft University of Technology), Georgios Portokalidis (Stevens Institute of Technology), Jun Xu (Stevens Institute of Technology, University of Utah)

Embedded devices are ubiquitous. However, preliminary evidence shows that attack mitigations protecting our desktops/servers/phones are missing in embedded devices, posing a significant threat to embedded security. To this end, this paper presents an in-depth study on the adoption of common attack mitigations on embedded devices. Precisely, it measures the presence of standard mitigations against memory corruptions in over 10k Linux-based firmware of deployed embedded devices.

The study reveals that embedded devices largely omit both user-space and kernel-level attack mitigations. The adoption rates on embedded devices are multiple times lower than their desktop counterparts. An equally important observation is that the situation is not improving over time. Without changing the current practices, the attack mitigations will remain missing, which may become a bigger threat in the upcoming IoT era.

Throughout follow-up analyses, we further inferred a set of factors possibly contributing to the absence of attack mitigations. The exemplary ones include massive reuse of non-protected software, lateness in upgrading outdated kernels, and restrictions imposed by automated building tools. We envision these will turn into insights towards improving the adoption of attack mitigations on embedded devices in the future.

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Ziqi Xu (University of Arizona), Jingcheng Li (University of Arizona), Yanjun Pan (University of Arizona), Loukas Lazos (University of Arizona, Tucson), Ming Li (University of Arizona, Tucson), Nirnimesh Ghose (University of Nebraska–Lincoln)

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ATTEQ-NN: Attention-based QoE-aware Evasive Backdoor Attacks

Xueluan Gong (Wuhan University), Yanjiao Chen (Zhejiang University), Jianshuo Dong (Wuhan University), Qian Wang (Wuhan University)

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Hongjun Choi (Purdue University), Zhiyuan Cheng (Purdue University), Xiangyu Zhang (Purdue University)

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Fine-Grained Coverage-Based Fuzzing

Bernard Nongpoh (Université Paris Saclay), Marwan Nour (Université Paris Saclay), Michaël Marcozzi (Université Paris Saclay), Sébastien Bardin (Université Paris Saclay)

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