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1. Shielding Software From Privileged Side-Channel Attacks

   Dong, Xiaowan; Shen, Zhuojia; Criswell, John; Cox, Alan L.; Dwarkada, Sandhya (August 2018, Proceedings of the Twenty Seventh Usenix Security Symposium)

   Commodity operating system (OS) kernels, such as Windows, Mac OS X, Linux, and FreeBSD, are susceptible to numerous security vulnerabilities. Their monolithic design gives successful attackers complete access to all application data and system resources. Shielding systems such as InkTag, Haven, and Virtual Ghost protect sensitive application data from compromised OS kernels. However, such systems are still vulnerable to side-channel attacks. Worse yet, compromised OS kernels can leverage their control over privileged hardware state to exacerbate existing side channels; recent work has shown that a compromised OS kernel can steal entire documents via side channels. This paper presents defenses against page table and last-level cache (LLC) side-channel attacks launched by a compromised OS kernel. Our page table defenses restrict the OS kernel’s ability to read and write page table pages and defend against page allocation attacks, and our LLC defenses utilize the Intel Cache Allocation Technology along with memory isolation primitives. We proto- type our solution in a system we call Apparition, building on an optimized version of Virtual Ghost. Our evaluation shows that our side-channel defenses add 1% to 18% (with up to 86% for one application) overhead to the optimized Virtual Ghost (relative to the native kernel) on real-world applications. 

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2. PREDATOR: A Cache Side-Channel Attack Detector Based on Precise Event Monitoring

   Minjun Wu, Stephen McCamant (September 2022, 2022 IEEE International Symposium on Secure and Private Execution Environment Design)

   Abstract—Recent work has demonstrated the security risk associated with micro-architecture side-channels. The cache timing side-channel is a particularly popular target due to its availability and high leakage bandwidth. Existing proposals for defending cache side-channel attacks either degrade cache performance and/or limit cache sharing, hence, should only be invoked when the system is under attack. A lightweight monitoring mechanism that detects malicious micro-architecture manipulation in realistic environments is essential for the judicious deployment of these defense mechanisms. In this paper, we propose PREDATOR, a cache side-channel attack detector that identifies cache events caused by an attacker. To detect side-channel attacks in noisy environments, we take advantage of the observation that, unlike non-specific noises, an active attacker alters victim’s micro-architectural states on security critical accesses and thus causes the victim extra cache events on those accesses. PREDATOR uses precise performance counters to collect detailed victim’s access information and analyzes location-based deviations. PREDATOR is capable of detecting five different attacks with high accuracy and limited performance overhead in complex noisy execution environments. PREDATOR remains effective even when the attacker slows the attack rate by 256 times. Furthermore, PREDATOR is able to accurately report details about the attack such as the instruction that accesses the attacked data. In the case of GnuPG RSA [20], PREDATOR can pinpoint the square/multiply operations in the Modulo-Reduce algorithm; and in the case of OpenSSL AES [45], it can identify the accesses to the Te-Table. 

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3. Stealthy Tracking of Autonomous Vehicles with Cache Side Channels

   Luo, Mulong Luo; Myers, Andrew C.; Suh, G. Edward (August 2000, USENIX Security Symposium)

   null (Ed.)

   Autonomous vehicles are becoming increasingly popular, but their reliance on computer systems to sense and operate in the physical world introduces new security risks. In this paper, we show that the location privacy of an autonomous vehicle may be compromised by software side-channel attacks if localization software shares a hardware platform with an attack program. In particular, we demonstrate that a cache side-channel attack can be used to infer the route or the location of a vehicle that runs the adaptive Monte-Carlo localization (AMCL) algorithm. The main contributions of the paper are as follows. First, we show that adaptive behaviors of perception and control algorithms may introduce new side-channel vulnerabilities that reveal the physical properties of a vehicle or its environment. Second, we introduce statistical learning models that infer the AMCL algorithm's state from cache access patterns and predict the route or the location of a vehicle from the trace of the AMCL state. Third, we implement and demonstrate the attack on a realistic software stack using real-world sensor data recorded on city roads. Our findings suggest that autonomous driving software needs strong timing-channel protection for location privacy. 

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4. Stealthy Tracking of Autonomous Vehicles with Cache Side Channels

   Luo, M Luo; Myers, A; Suh, G (August 2020, 29th USENIX Security Symposium (USENIX Security 20))

   Autonomous vehicles are becoming increasingly popular, but their reliance on computer systems to sense and operate in the physical world introduces new security risks. In this paper, we show that the location privacy of an autonomous vehicle may be compromised by software side-channel attacks if localization software shares a hardware platform with an attack program. In particular, we demonstrate that a cache side-channel attack can be used to infer the route or the location of a vehicle that runs the adaptive Monte-Carlo localization (AMCL) algorithm. The main contributions of the paper are as follows. First, we show that adaptive behaviors of perception and control algorithms may introduce new side-channel vulnerabilities that reveal the physical properties of a vehicle or its environment. Second, we introduce statistical learning models that infer the AMCL algorithm's state from cache access patterns and predict the route or the location of a vehicle from the trace of the AMCL state. Third, we implement and demonstrate the attack on a realistic software stack using real-world sensor data recorded on city roads. Our findings suggest that autonomous driving software needs strong timing-channel protection for location privacy. 

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5. A Cross-Platform Cache Timing Attack Framework via Deep Learning

   https://doi.org/10.23919/DATE54114.2022.9774612  

   Ding, Ruyi; Zhang, Ziyue; Zhang, Xiang; Gongye, Cheng; Fei, Yunsi; Ding, Aidong A. (March 2022, IEEE Design Automation and Test in Europe 2-22)

   While deep learning methods have been adopted in power side-channel analysis, they have not been applied to cache timing attacks due to the limited dimension of cache timing data. This paper proposes a persistent cache monitor based on cache line flushing instructions, which runs concurrently to a victim execution and captures detailed memory access patterns in high- dimensional timing traces. We discover a new cache timing side- channel across both inclusive and non-inclusive caches, different from the traditional “Flush+Flush” timing leakage. We then propose a non-profiling differential deep learning analysis strategy to exploit the cache timing traces for key recovery. We further propose a framework for cross-platform cache timing attack via deep learning. Knowledge learned from profiling a common reference device can be transferred to build models to attack many other victim devices, even in different processor families. We take the OpenSSL AES-128 encryption algorithm as an example victim and deploy an asynchronous cache attack. We target three different devices from Intel, AMD, and ARM processors. We examine various scenarios for assigning the teacher role to one device and the student role to other devices, and evaluate the cross- platform deep-learning attack framework. Experimental results show that this new attack is easily extendable to victim devices • and is more effective than attacks without any prior knowledge. 

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