Co-location detection on the Cloud

In this work we focus on the problem of co-location as a first step of conducting Cross-VM attacks such as Prime and Probe or Flush+Reload in commercial clouds. We demonstrate and compare three co-location detection methods namely, cooperative Last-Level Cache (LLC) covert channel, software profiling on the LLC and memory bus locking. We conduct our experiments on three commercial clouds, Amazon EC2, Google Compute Engine and Microsoft Azure. Finally, we show that both cooperative and non-cooperative co-location to specific targets on cloud is still possible on major 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 In a preparatory step, we reverse engineer the unpublished nonlinear slice selection function for the 10 core Intel Xeon processor which significantly accelerates our attack (this chipset is used in Amazon EC2).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

MicroWalk: A Framework for Finding Side Channels in Binaries

Microarchitectural side channels expose unprotected software to information leakage attacks where a software adversary is able to track runtime behavior of a benign process and steal secrets such as cryptographic keys. As suggested by incremental software patches for the RSA algorithm against variants of side-channel attacks within different versions of cryptographic libraries, protecting security-critical algorithms against side channels is an intricate task. Software protections avoid leakages by operating in constant time with a uniform resource usage pattern independent of the processed secret. In this respect, automated testing and verification of software binaries for leakage-free behavior is of importance, particularly when the source code is not available. In this work, we propose a novel technique based on Dynamic Binary Instrumentation and Mutual Information Analysis to efficiently locate and quantify memory based and control-flow based microarchitectural leakages. We develop a software framework named \tool~for side-channel analysis of binaries which can be extended to support new classes of leakage. For the first time, by utilizing \tool, we perform rigorous leakage analysis of two widely-used closed-source cryptographic libraries: \emph{Intel IPP} and \emph{Microsoft CNG}. We analyze $15$ different cryptographic implementations consisting of $112$ million instructions in about $105$ minutes of CPU time. By locating previously unknown leakages in hardened implementations, our results suggest that \tool~can efficiently find microarchitectural leakages in software binaries