Transient execution vulnerabilities have emerged as a critical threat to modern processors. Hardware fuzzing testing techniques have recently shown promising results in discovering transient execution bugs in large-scale out-of-order processor designs. However, their poor microarchitectural control- lability and observability prevent them from effectively and efficiently detecting transient execution vulnerabilities.This paper proposes DejaVuzz, a novel pre-silicon stage processor transient execution bug fuzzer. DejaVuzz utilizes two innovative operating primitives: dynamic swappable memory and differential information flow tracking, enabling more effective and efficient transient execution vulnerability detection. The dynamic swappable memory enables the isolation of different instruction streams within the same address space. Leveraging this capability, DejaVuzz generates targeted training for arbitrary transient windows and eliminates ineffective training, enabling efficient triggering of diverse transient windows. The differential information flow tracking aids in observing the propagation of sensitive data across the microarchitecture. Based on taints, DejaVuzz designs the taint coverage matrix to guide mutation and uses taint liveness annotations to identify exploitable leak- ages. Our evaluation shows that DejaVuzz outperforms the state-of-the-art fuzzer SpecDoctor, triggering more com- prehensive transient windows with lower training overhead and achieving a 4.7x coverage improvement. And DejaVuzz also mitigates control flow over-tainting with acceptable overhead and identifies 5 previously undiscovered transient execution vulnerabilities (with 6 CVEs assigned) on BOOM and XiangShan.
View on arXiv@article{xu2025_2504.20934,
title={ DejaVuzz: Disclosing Transient Execution Bugs with Dynamic Swappable Memory and Differential Information Flow Tracking assisted Processor Fuzzing },
author={ Jinyan Xu and Yangye Zhou and Xingzhi Zhang and Yinshuai Li and Qinhan Tan and Yinqian Zhang and Yajin Zhou and Rui Chang and Wenbo Shen },
journal={arXiv preprint arXiv:2504.20934},
year={ 2025 }
}