Johannes Lenzen (Technical University of Darmstadt), Mohamadreza Rostami (Technical University of Darmstadt), Lichao Wu (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

Modern Central Processing Units (CPUs) are black boxes, proprietary, and increasingly characterized by sophisticated microarchitectural flaws that evade traditional analysis. While some of these critical vulnerabilities have been uncovered through cumbersome manual effort, building an automated and systematic vulnerability detection framework for real-world post-silicon processors remains a challenge.

In this paper, we present Fuzzilicon, the first post-silicon fuzzing framework for real-world x86 CPUs that brings deep introspection into the microcode and microarchitectural layers. Fuzzilicon automates the discovery of vulnerabilities that were previously only detectable through extensive manual reverse engineering, and bridges the visibility gap by introducing microcode-level instrumentation. At the core of Fuzzilicon is a novel technique for extracting feedback directly from the processor’s microarchitecture, enabled by reverse-engineering Intel’s proprietary microcode update interface. We develop a minimally intrusive instrumentation method and integrate it with a hypervisor-based fuzzing harness to enable precise, feedback-guided input generation, without access to Register Transfer Level (RTL) or vendor support.

Applied to Intel’s Goldmont microarchitecture, Fuzzilicon introduces 5 significant findings, including two previously unknown microcode-level speculative-execution vulnerabilities. Besides, the Fuzzilicon framework automatically rediscover the µSpectre class of vulnerabilities, which were detected manually in the previous work. Fuzzilicon reduces coverage collection overhead by up to 31× compared to baseline techniques and achieves 16.27% unique microcode coverage of hookable locations, the first empirical baseline of its kind. As a practical, coverage-guided, and scalable approach to post-silicon fuzzing, Fuzzilicon establishes a new foundation to automate the discovery of complex CPU vulnerabilities.

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Cascading and Proxy Membership Inference Attacks

Yuntao Du (Purdue University), Jiacheng Li (Purdue University), Yuetian Chen (Purdue University), Kaiyuan Zhang (Purdue University), Zhizhen Yuan (Purdue University), Hanshen Xiao (Purdue University and NVIDIA Research), Bruno Ribeiro (Purdue University), Ninghui Li (Purdue University)

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MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness

Xiaoyun xu (Radboud University), Shujian Yu (Vrije Universiteit Amsterdam), Zhuoran Liu (Radboud University), Stjepan Picek (Radboud University)

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Non-Disruptive Disruption: An Empirical Experience of Introducing LLMs in...

Francis Hahn (University of South Florida), Mohd Mamoon (University of Kansas), Alexandru G. Bardas (University of Kansas), Michael Collins (University of Southern California – ISI), Jaclyn Lauren Dudek (University of Kansas), Daniel Lende (University of South Florida), Xinming Ou (University of South Florida), S. Raj Rajagopalan (Resideo Technologies)

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