Publication

Abstract : Speculative execution increases the performance of the processor by predicting and executing instructions out of order, allowing for faster computation in modern CPUs. However, it introduces critical vulnerabilities that enable transient attacks to leak sensitive data through covert hardware channels, exploiting mechanisms like branch misprediction and out-of-order execution. This project studies the time taken in different cases, both with and without LFENCE, with detailed analysis. LFENCE acts as a speculation barrier to halt risky predictions and mitigate timing leaks. Our results highlight the differences in execution times, providing insights into how speculation influences cache behavior and creates exploitable gaps. Using Python-based visualizations, we map these timing patterns to offer clear insights into vulnerabilities and defenses. By incorporating various defense mechanisms, including LFENCE and other hardware security strategies, this study demonstrates their role in reducing transient leaks, offering a practical understanding of execution timing impacts and performance trade-offs. This accessible demonstration bridges theoretical hardware security concepts with real-world observations, emphasizing effective countermeasures without requiring extensive hardware modifications.