Stephan Havermans (IMDEA Software Institute), Lars Baumgaertner, Jussi Roberts, Marcus Wallum (European Space Agency), Juan Caballero (IMDEA Software Institute)

Space systems are critical assets and protecting them against cyberattacks is a paramount challenge that has received limited attention. In particular, it is fundamental to secure spacecraft communications by identifying and removing potential vulnerabilities in the implementations of space (communication) protocols, which could be remotely exploited by attackers. This work reports our preliminary experiences when fuzzing five open-source implementations of four space protocols using two approaches: grammar-based fuzzing and coverageguided fuzzing. To enable the fuzzing, we created grammars for the protocols and custom harnesses for the targets. Our fuzzing identified 11 vulnerabilities across four targets caused by typical memory-related bugs such as double-frees, out-of-bounds reads, and the use of uninitialized variables. We responsibly disclosed the vulnerabilities. To date, 5 vulnerabilities have been patched and 4 have been awarded CVE identifiers. Additionally, we discovered a discrepancy in how one target interprets a protocol standard, which we reported and has since been fixed.

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LADDER: Multi-Objective Backdoor Attack via Evolutionary Algorithm

Dazhuang Liu (Delft University of Technology), Yanqi Qiao (Delft University of Technology), Rui Wang (Delft University of Technology), Kaitai Liang (Delft University of Technology), Georgios Smaragdakis (Delft University of Technology)

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BitShield: Defending Against Bit-Flip Attacks on DNN Executables

Yanzuo Chen (The Hong Kong University of Science and Technology), Yuanyuan Yuan (The Hong Kong University of Science and Technology), Zhibo Liu (The Hong Kong University of Science and Technology), Sihang Hu (Huawei Technologies), Tianxiang Li (Huawei Technologies), Shuai Wang (The Hong Kong University of Science and Technology)

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CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian...

Kaiyuan Zhang (Purdue University), Siyuan Cheng (Purdue University), Guangyu Shen (Purdue University), Bruno Ribeiro (Purdue University), Shengwei An (Purdue University), Pin-Yu Chen (IBM Research AI), Xiangyu Zhang (Purdue University), Ninghui Li (Purdue University)

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THEMIS: Regulating Textual Inversion for Personalized Concept Censorship

Yutong Wu (Nanyang Technological University), Jie Zhang (Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore), Florian Kerschbaum (University of Waterloo), Tianwei Zhang (Nanyang Technological University)

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