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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) onmore »real-world applications.« less
2. ENCIDER: Detecting Timing and Cache Side Channels in SGX Enclaves and Cryptographic APIs

   https://doi.org/10.1109/TDSC.2022.3160346  

   Yavuz, Tuba ; Fowze, Farhaan ; Hernandez, Grant ; Bai, Ken Yihang ; Butler, Kevin ; Tian, Dave Jing ( March 2022 , IEEE Transactions on Dependable and Secure Computing)

   Confidential computing aims to secure the code and data in use by providing a Trusted Execution Environment (TEE) for applications using hardware features such as Intel SGX.Timing and cache side-channel attacks, however, are often outside the scope of the threat model, although once exploited they are able to break all the default security guarantees enforced by hardware. Unfortunately, tools detecting potential side-channel vulnerabilities within applications are limited and usually ignore the strong attack model and the unique programming model imposed by Intel SGX. This paper proposes a precise side-channel analysis tool, ENCIDER, detecting both timing and cache side-channel vulnerabilities within SGX applications via inferring potential timing observation points and incorporating the SGX programming model into analysis. ENCIDER uses dynamic symbolic execution to decompose the side-channel requirement based on the bounded non-interference property and implements byte-level information flow tracking via API modeling. We have applied ENCIDER to 4 real-world SGX applications, 2 SGX crypto libraries, and 3 widely-used crypto libraries, and found 29 timing side channels and 73 code and data cache side channels. We have reported our findings to the corresponding parties, e.g., Intel and ARM, who have confirmed most of the vulnerabilities detected.
3. Robust Website Fingerprinting Through the Cache Occupancy Channel

   Shusterman, Anatoly ; Kang, Lachlan ; Haskal, Yarden ; Meltser, Yosef ; Mittal, Prateek ; Oren, Yossef ; Yarom, Yuval ( August 2019 , USENIX Security Symposium)

   Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who can observe all traffic traveling between the user’s computer and the secure network. In this work we investigate these attacks under a different attack model, in which the adversary is capable of sending a small amount of malicious JavaScript code to the target user’s computer. The malicious code mounts a cache side-channel attack, which exploits the effects of contention on the CPU’s cache, to identify other websites being browsed. The effectiveness of this attack scenario has never been systematically analyzed, especially in the open-world model which assumes that the user is visiting a mix of both sensitive and non-sensitive sites. We show that cache website fingerprinting attacks in JavaScript are highly feasible. Specifically, we use machine learning techniques to classify traces of cache activity. Unlike prior works, which try to identify cache conflicts, our work measures the overall occupancy of the last-level cache. We show that our approach achieves high classification accuracy inmore »both the open-world and the closed-world models. We further show that our attack is more resistant than network-based fingerprinting to the effects of response caching, and that our techniques are resilient both to network-based defenses and to side-channel countermeasures introduced to modern browsers as a response to the Spectre attack. To protect against cache-based website fingerprinting, new defense mechanisms must be introduced to privacy-sensitive browsers and websites. We investigate one such mechanism, and show that generating artificial cache activity reduces the effectiveness of the attack and completely eliminates it when used in the Tor Browser« less
4. Composable Cachelets: Protecting Enclaves from Cache Side-Channel Attacks

   Townley, Daniel ; Arikan, Kerem ; Liu, Yu David ; Ponomarev, Dmitry ; Ergin, Ogiz ( July 2022 , 2022 USENIX Security Symposium)

   The security of isolated execution architectures such as Intel SGX has been significantly threatened by the recent emer- gence of side-channel attacks. Cache side-channel attacks allow adversaries to leak secrets stored inside isolated en- claves without having direct access to the enclave memory. In some cases, secrets can be leaked even without having the knowledge of the victim application code or having OS-level privileges. We propose the concept of Composable Cachelets (CC), a new scalable strategy to dynamically partition the last-level cache (LLC) for completely isolating enclaves from other applications and from each other. CC supports enclave isolation in caches with the capability to dynamically readjust the cache capacity as enclaves are created and destroyed. We present a cache-aware and enclave-aware operational seman- tics to help rigorously establish security properties of CC, and we experimentally demonstrate that CC thwarts side-channel attacks on caches with modest performance and complexity impact.
5. 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.