Stuxnet was the first targeted malware that received worldwide attention forcausing physical damage in an industrial infrastructure seemingly isolated from the onlineworld. Stuxnet was a powerful targeted cyber-attack, and soon other malware samples were discovered that belong to this family. In this paper, we will first present our analysis of Duqu, an information-collecting malware sharing striking similarities with Stuxnet. Wedescribe our contributions in the investigation ranging from the original detection of Duquvia finding the dropper file to the design of a Duqu detector toolkit. We then continue with the analysis of the Flame advanced information-gathering malware. Flame is unique in thesense that it used advanced cryptographic techniques to masquerade as a legitimate proxyfor the Windows Update service. We also present the newest member of the family, called Gauss, whose unique feature is that one of its modules is encrypted such that it can onlybe decrypted on its target system; hence, the research community has not yet been able to analyze this module. For this particular malware, we designed a Gauss detector serviceand we are currently collecting intelligence information to be able to break its very specialencryption mechanism. Besides explaining the operation of these pieces of malware, wealso examine if and how they could have been detected by vigilant system administrators manually or in a semi-automated manner using available tools. Finally, we discuss lessonsthat the community can learn from these incidents. We focus on technical issues, and avoidspeculations on the origin of these threats and other geopolitical questions

Virtualization is a powerful technology for increasing the efficiency of computing services; however, besides its advantages, it also raises a number of security issues. In this article, we provide a thorough survey of those security issues in hardware virtualization. We focus on potential vulnerabilities and existing attacks on various virtualization platforms, but we also briefly sketch some possible countermeasures. To the best of our knowledge, this is the first survey of security issues in hardware virtualization with this level of details. Moreover, the adversary model and the structuring of the attack vectors are original contributions, never published before.

The security of virtual machine monitors (VMMs) is a challenging and active field of research. In particular, due to the increasing significance of hardware virtualization in cloud solutions, it is important to clearly understand existing and arising VMM-related threats. Unfortunately, there is still a lot of confusion around this topic as many attacks presented in the past have never been implemented in practice or tested in a realistic scenario.In this paper, we shed light on VM related threats and defences by implementing, testing, and categorizing a wide range of known and unknown attacks based on directly assigned devices. We executed these attacks on an exhaustive set of VMM configurations to determine their potential impact. Our experiments suggest that most of the previously known attacks are ineffective in current VMM setups.We also developed an automatic tool, called PTFuzz, to discover hardware-level problems that affects current VMMs. By using PTFuzz, we found several cases of unexpected hardware behaviour, and a major vulnerability on Intel platforms that potentially impacts a large set of machines used in the wild. These vulnerabilities affect unprivileged virtual machines that use a directly assigned device (e.g., network card) and have all the existing hardware protection mechanisms enabled. Such vulnerabilities either allow an attacker to generate a host-side interrupt or hardware faults, violating expected isolation properties. These can cause host software (e.g., VMM) halt as well as they might open the door for practical VMM exploitations.We believe that our study can help cloud providers and researchers to better understand the limitations of their current architectures to provide secure hardware virtualization and prepare for future attacks.

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Malware analysis can be an efficient way to combat malicious code, however, miscreants are constructing heavily armoured samples in order to stymie the observation of their artefacts. Security practitioners make heavy use of various virtualization techniques to create sandboxing environments that provide a certain level of isolation between the host and the code being analysed. However, most of these are easy to be detected and evaded. The introduction of hardware assisted virtualization (Intel VT and AMD-V) made the creation of novel, out-of-the-guest malware analysis platforms possible. These allow for a high level of transparency by residing completely outside the guest operating system being examined, thus conventional in-memory detection scans are ineffective. Furthermore, such analyzers resolve the shortcomings that stem from inaccurate system emulation, inguest timings, privileged operations and so on.In this paper, we introduce novel approaches that make the detection of hardware assisted virtualization platforms and out-of-the-guest malware analysis frameworks possible. To demonstrate our concepts, we implemented an application framework called nEther that is capable of detecting the out-of-the-guest malware analysis framework Ether [6].