Control-flow hijacking and data-oriented attacks are becoming more sophisticated. These attacks, especially data-oriented attacks, can result in critical security threats, such as leaking an SSL key. Data-oriented attacks are hard to defend against with acceptable performance due to the sheer amount of data pointers present. The root cause of such attacks is using pointers in unintended ways; fundamentally, these attacks rely on abusing pointers to violate the original scope they were used in or the original types that they were declared as.

System calls are a critical building block in many serious security attacks, such as control-flow hijacking and privilege escalation attacks. Security-sensitive system calls (e.g., execve, mprotect), especially play a major role in completing attacks. Yet, few defense efforts focus to ensure their legitimate usage, allowing attackers to maliciously leverage system calls in attacks.

Most modern software attacks are rooted in memory corruption vulnerabilities, which are capable of altering security-sensitive data (e.g., function pointers) to unintended values. This paper introduces a new security property, the Value Invariant Property (VIP), and HyperSpace, our prototype that enforces VIP on security-sensitive data. HyperSpace safeguards the integrity of "data values" instead of enforcing control/data flow, allowing for low runtime overhead, yet defeating critical attacks effectively. We implement four representative security policies including Control Flow Integrity (VIP-CFI), Code Pointer Integrity (VIP-CPI), Virtual function Table protection (VIP-VTPtr), and heap metadata protection based on HyperSpace. We evaluate HyperSpace with SPEC CPU2006 benchmarks and real-world applications (NGINX and PostgreSQL) and test how HyperSpace defeats memory corruption-based attacks, including three real-world exploits and six attacks that bypass existing defenses (COOP, heap exploits, etc.). Our experimental evaluation shows that HyperSpace successfully stops all these attacks with low runtime overhead: 0.88% and 6.18% average performance overhead for VIP-CFI and VIP-CPI, respectively, and overall approximately 13.18% memory overhead with VIP-CPI in SPEC CPU2006.

Defense techniques such as Data Execution Prevention (DEP) and Address Space Layout Randomization (ASLR) were role models in preventing early return-oriented programming (ROP) attacks by keeping performance and scalability in the forefront, making them widely-adopted. As code reuse attacks evolved in complexity, defenses have lost touch with pragmatic defense design to ensure security, either being narrow in scope or providing unrealistic overheads.

Energy efficiency is one of the most important design considerations in running modern datacenters. Datacenter operating systems rely on software techniques such as execution migration to achieve energy efficiency across pools of machines. Execution migration is possible in datacenters today because they consist mainly of homogeneous-ISA machines. However, recent market trends indicate that alternate ISAs such as ARM and PowerPC are pushing into the datacenter, meaning current execution migration techniques are no longer applicable. How can execution migration be applied in future heterogeneous-ISA datacenters?

The recent possibility of integrating multiple-OS-capable, high-core-count, heterogeneous-ISA processors in the same platform poses a question: given the tight integration between system components, can a shared memory programming model be adopted, enhancing programmability? If this can be done, an enormous amount of existing code written for shared memory architectures would not have to be rewritten to use a new programming paradigm (e.g., code offloading) that is often very expensive and error prone. We propose a new software architecture that is composed of an operating system and a compiler framework to run ordinary shared memory applications, written for homogeneous machines, on OS-capable heterogeneous-ISA machines. Applications run transparently amongst different ISA processors while exploiting the most optimized instruction set for each code block. We have implemented and tested our system, called Popcorn, on a multi-core Intel Xeon machine with a PCIe Intel Xeon Phi to demonstrate the viability of our approach. Application execution on Popcorn demonstrates to be up to 52% faster than the most performant native execution on Linux, on either Xeon or Xeon Phi, while removing the burden of the programmer having to adopt a different programming model than shared memory on a heterogeneous system. When compared to an offloading programming model, Popcorn is shown to be up to 6.2 times faster.