MASCAT: Stopping Microarchitectural Attacks Before Execution

Microarchitectural attacks have gained popularity in recent years since they use only standard resources, e.g. memory and cache access timing. Such privileges are available to applications at the lowest privilege levels. Further, microarchitectural attacks have proven successful on shared cloud instances across VMs, on smartphones with sandboxing, and on numerous embedded platforms. Given the rise of malicious code in app stores and in online repositories it becomes essential to scan applications for such stealthy attacks. We present a static code analysis tool, MASCAT , capable of scanning for ever evolving microarchitectural attacks. Our proposed tool MASCAT can be used by app store service providers to perform large scale fully automated analysis of applications. The initial MASCAT suite is built to include attack vectors to cover popular cache/DRAM access attacks and Rowhammer. Further, our tool is easily extensible to cover newer attack vectors as they emerge

Systematic Reverse Engineering of Cache Slice Selection in Intel Processors

Dividing last level caches into slices is a popular method to prevent memory accesses from becoming a bottleneck on modern multicore processors. In order to assess and understand the benefits of cache slicing in detail, a precise knowledge of implementation details such as the slice selection algorithm are of high importance. However, slice selection methods are mostly unstudied, and processor manufacturers choose not to publish their designs, nor their design rationale. In this paper, we present a tool that allows to recover the slice selection algorithm for Intel processors. The tool uses cache access information to derive equations that allow the reconstruction of the applied slice selection algorithm. Thereby, the tool provides essential information for performing last level cache attacks and enables further exploration of the behavior of modern caches. The tool is successfully applied to a range of Intel CPUs with different slices and architectures. Results show that slice selection algorithms have become more complex over time by involving an increasing number of bits of the physical address. We also demonstrate that among the most recent processors, the slice selection algorithm depends on the number of CPU cores rather than the processor model

Jackpot Stealing Information From Large Caches via Huge Pages

The cloud computing infrastructure relies on virtualized servers that provide isolation across guest OS\u27s through sandboxing. This isolation was demonstrated to be imperfect in past work which exploited hardware level information leakages to gain access to sensitive information across co-located virtual machines (VMs). In response virtualization companies and cloud services providers have disabled features such as deduplication to prevent such attacks. In this work, we introduce a fine-grain cross-core cache attack that exploits access time variations on the last level cache. The attack exploits huge pages to work across VM boundaries without requiring deduplication. No configuration changes on the victim OS are needed, making the attack quite viable. Furthermore, only machine co-location is required, while the target and victim OS can still reside on different cores of the machine. Our new attack is a variation of the prime and probe cache attack whose applicability at the time is limited to L1 cache. In contrast, our attack works in the spirit of the flush and reload attack targeting the shared L3 cache instead. Indeed, by adjusting the huge page size our attack can be customized to work virtually at any cache level/size. We demonstrate the viability of the attack by targeting an OpenSSL1.0.1f implementation of AES. The attack recovers AES keys in the cross-VM setting on Xen 4.1 with deduplication disabled, being only slightly less efficient than the flush and reload attack. Given that huge pages are a standard feature enabled in the memory management unit of OS\u27s and that besides co-location no additional assumptions are needed, the attack we present poses a significant risk to existing cloud servers

Fine grain Cross-VM Attacks on Xen and VMware are possible!

This work exposes further vulnerabilities in virtualized cloud servers by mounting Cross-VM cache attacks in Xen and VMware VMs targeting AES running in the victim VM. Even though there exists a rich literature on cache attacks on AES, so far only a single work, demonstrating a working attack on an ARM platform running a L4Re virtualization layer has been published. Here we show that AES in a number popular cryptographic libraries including OpenSSL, PolarSSL and Libgcrypt are vulnerable to Bernstein’s correlation attack when run in Xen and VMware (bare metal version) VMs, the most popular VMs used by cloud service providers (CSP) such as Amazon and Rackspace. We also show that the vulnerability persists even if the VMs are placed on different cores in the same machine. The results of this study shows that there is a great security risk to AES and (data encrypted under AES) on popular cloud services

Seriously, get off my cloud! Cross-VM RSA Key Recovery in a Public Cloud

It has been six years since Ristenpart et al. demonstrated the viability of co-location and provided the first concrete evidence for sensitive information leakage on a commercial cloud. We show that co-location can be achieved and detected by monitoring the last level cache in public clouds. More significantly, we present a full-fledged attack that exploits subtle leakages to recover RSA decryption keys from a co-located instance. We target a recently patched Libgcrypt RSA implementation by mounting Cross-VM Prime and Probe cache attacks in combination with other tests to detect co-location in Amazon EC2. In a preparatory step, we reverse engineer the unpublished nonlinear slice selection function for the 10 core Intel Xeon processor which significantly accelerates our attack (this chipset is used in Amazon EC2). After co-location is detected and verified, we perform the Prime and Probe attack to recover noisy keys from a carefully monitored Amazon EC2 VM running the aforementioned vulnerable libgcrypt library. We subsequently process the noisy data and obtain the complete 2048-bit RSA key used during encryption. This work reaffirms the privacy concerns and underlines the need for deploying stronger isolation techniques in public clouds

Cache Attacks Enable Bulk Key Recovery on the Cloud

Cloud services keep gaining popularity despite the security concerns. While non-sensitive data is easily trusted to cloud, security critical data and applications are not. The main concern with the cloud is the shared resources like the CPU, memory and even the network adapter that provide subtle side-channels to malicious parties. We argue that these side-channels indeed leak fine grained, sensitive information and enable key recovery attacks on the cloud. Even further, as a quick scan in one of the Amazon EC2 regions shows, high percentage -55\%- of users run outdated, leakage prone libraries leaving them vulnerable to mass surveillance. The most commonly exploited leakage in the shared resource systems stem from the cache and the memory. High resolution and the stability of these channels allow the attacker to extract fine grained information. In this work, we employ the \PnP\ attack to retrieve an RSA secret key from a co-located instance. To speed up the attack, we reverse engineer the cache slice selection algorithm for the Intel Xeon E5-2670 v2 that is used in our cloud instances. Finally we employ noise reduction to deduce the RSA private key from the monitored traces. By processing the noisy data we obtain the complete 2048-bit RSA key used during the decryption