Mitigating microarchitectural side channels remains a central challenge in hardware security. Despite substantial research efforts, current defenses are often narrowly tailored to specific vulnerabilities, leaving systems exposed to a broader spectrum of microarchitectural side-channel attacks. In this paper, we propose a generic approach to mitigate side-channel attacks with minimal architectural changes. Unlike traditional approaches that focus on mitigating specific side channels, we propose a dynamic strategy that alters the decoding of the instructions into secure (side-channel resilient) or performance versions of the instructions, based on the data it is processing. Specifically, to minimize performance overhead, decoding to a secure version is selectively applied only when sensitive data are being processed, ensuring optimal performance for instructions operating on non-sensitive data.To evaluate our approach, we implement it on the RISC-V out-of-order BOOM processor. Our results demonstrate that the mechanism increases the utilization of FPGA resources by only 2% compared to the original design. Furthermore, it imposes 0% performance overhead for unprotected applications, while maintaining overhead between up to 25% for security-critical workloads. This work represents a scalable and efficient solution for defending against micro-architectural side-channel attacks without compromising system performance.

Cloud computing has become crucial for the commercial world due to its computational capacity, storage capabilities, scalability, software integration, and billing convenience. Initially, clouds were relatively homogeneous, but now diverse machine configurations in heterogeneous clouds are recognized for their improved application performance and energy efficiency. This shift is driven by the integration of various hardware to accommodate diverse user applications. However, alongside these advancements, security threats like micro-architectural attacks are increasing concerns for cloud providers and users. Studies like Repttack and Cloak & Co-locate highlight the vulnerability of heterogeneous clouds to co-location attacks, where attacker and victim instances are placed together. The ease of these attacks isn’t solely linked to heterogeneity but also correlates with how heterogeneous the target systems are. Despite this, no numerical metrics exist to quantify cloud heterogeneity. This article introduces the Heterogeneity Score (HeteroScore) to evaluate server setups and instances. HeteroScore significantly correlates with co-location attack security. The article also proposes strategies to balance diversity and security. This study pioneers the quantitative analysis connecting cloud heterogeneity and infrastructure security.

Machine Learning (ML) security practices include hiding ML model architectures to protect intellectual property and prevent attacks. We introduce a novel fingerprinting attack using frequency throttling-based Side-Channel Attack (SCA) to detect an ML model's architecture family by converting power side-channel data into timing variations. This method involves using adversary kernels and a time series ML classifier to discern the architecture from execution time patterns during model operation. We achieved up to 96% accuracy in identifying known ML models' architecture families under Ring 0 privileges and we demonstrated its effectiveness across different platforms. Moreover, our code is publicly available 1.

Transient execution attacks pose information leakage risks in current systems. Disabling speculative execution, though mitigating the issue, results in significant performance loss. Accurate identification of vulnerable gadgets is essential for balancing security and performance. However, uncovering all covert channels is challenging due to complex microarchitectural analysis. This paper introduces SpecScope, a framework for automating the detection of Spectre gadgets in code using a black-box microarchitecture approach. SpecScope focuses on contention between transient and non-transient instructions to precisely identify and reduce false-positive Spectre gadgets, minimizing mitigation overhead. Tested on public libraries, SpecScope outperforms existing methods, reducing False-Positive rates by 8.9% and increasing True-Positive rates by 10.4%.