Nowadays, cars are equipped with hundreds of sensors and dozens of computers that process data. Unfortunately, due to the very secret nature of the automotive industry, there is no official nor objective source of information as to what data exactly their vehicles collect. Anecdotal evidence suggests that OEMs are collecting huge amounts of personal data about their drivers, which they suddenly reveal when requested in court.In this paper, we present our tool AutoCAN for privacy and security analysis of cars that reveals what data cars collect by tapping into in-vehicle networks and extracting time series of data and automatically making sense of them by establishing relationships based on laws of physics. These algorithms work irrespective of make, model or used protocols. Our results show that car makers track the GPS position, the number of occupants, their weight, usage statistics of doors, lights, and AC. We also reveal that OEMs embed functions to remotely disable the car or get an alert when the driver is speeding.

We present RamCrypt, a solution that allows unmodified Linux processes to transparently work on encrypted data. RamCrypt can be deployed and enabled on a per-process basis without recompiling user-mode applications. In every enabled process, data is only stored in cleartext for the moment it is processed, and otherwise stays encrypted in RAM. In particular, the required encryption keys do not reside in RAM, but are stored in CPU registers only. Hence, RamCrypt effectively thwarts memory disclosure attacks, which grant unauthorized access to process memory, as well as physical attacks such as cold boot and DMA attacks. In its default configuration, RamCrypt exposes only up to 4 memory pages in cleartext at the same time. For the nginx web server serving encrypted HTTPS pages under heavy load, the necessary TLS secret key is hidden for 97% of its time.

Code reuse attacks allow an adversary to impose malicious behavior on an otherwise benign program. To mitigate such attacks, a common approach is to disguise the address or content of code snippets by means of randomization or rewriting, leaving the adversary with no choice but guessing. However, disclosure attacks allow an adversary to scan a process - even remotely - and enable her to read executable memory on-the-fly, thereby allowing the just-in time assembly of exploits on the target site. In this paper, we propose an approach that fundamentally thwarts the root cause of memory disclosure exploits by preventing the inadvertent reading of code while the code itself can still be executed. We introduce a new primitive we call Execute-no-Read (XnR) which ensures that code can still be executed by the processor, but at the same time code cannot be read as data. This ultimately forfeits the self-disassembly which is necessary for just-in-time code reuse attacks (JIT-ROP) to work. To the best of our knowledge, XnR is the first approach to prevent memory disclosure attacks of executable code and JIT-ROP attacks in general. Despite the lack of hardware support for XnR in contemporary Intel x86 and ARM processors, our software emulations for Linux and Windows have a run-time overhead of only 2.2% and 3.4%, respectively.

Code reuse attacks such as return-oriented programming are one of the most powerful threats to contemporary software. ASLR was introduced to impede these attacks by dispersing shared libraries and the executable in memory. However, in practice its entropy is rather low and, more importantly, the leakage of a single address reveals the position of a whole library in memory. The recent mitigation literature followed the route of randomization, applied it at different stages such as source code or the executable binary. However, the code segments still stay in one block. In contrast to previous work, our randomization solution, called Xifer, (1) disperses all code (executable and libraries) across the whole address space, (2) re-randomizes the address space for each run, (3) is compatible to code signing, and (4) does neither require offline static analysis nor source-code. Our prototype implementation supports the Linux ELF file format and covers both mainstream processor architectures x86 and ARM. Our evaluation demonstrates that Xifer performs efficiently at load- and during run-time (1.2% overhead).

Cloud Computing is an emerging technology promising new business opportunities and easy deployment of web services. Much has been written about the risks and benefits of cloud computing in the last years. The literature on clouds often points out security and privacy challenges as the main obstacles, and proposes solutions and guidelines to avoid them. However, most of these works deal with either malicious cloud providers or customers, but ignore the severe threats caused by unaware users.

In this paper we present a secure micro kernel architecture (called Ray) that was designed from scratch with security goals in mind. It features some traditional security aspects like process isolation, advanced non-standard security aspects like padded non-readable memory boundaries and new contributions like memory gifts and behaviour deviation detection. This theoretical design has been implemented as a proof of concept for x86 based processors including a small set of essential drivers and user land applications in order to verify and test the claims made herein.