Kernel memory allocators maintain several metadata structures optimized for efficiently managing system memory. However, existing implementations adopt either weak or no protection at all to ensure the integrity of said metadata in the presence of memory errors. In this paper, we first demonstrate how existing memory hardening schemes fall short against several in-kernel memory corruption scenarios. We then present ISLAB: a set of novel (slab-based) heap hardening techniques that aim to ensure the integrity of the memory managed by the kernel, and minimize the incurred runtime, and memory, overhead. ISLAB prevents memory corruption exploits by segregating metadata from within corruptible memory objects into shadow memory. It also relies on a novel SMAP-assisted memory isolation framework, called kSMAP, to protect allocator metadata against adversaries with stronger memory access capabilities. We implemented and evaluated ISLAB atop SLUB, the default slab allocator in Linux, and equipped it with kSMAP to protect process credentials, a popular target in kernel exploitation. Our experiments show that ISLAB incurs no runtime overhead in realistic benchmarks, and moderate overhead in stress tests. Lastly, we show how ISLAB's approach can be generalized to protect the integrity of other kernel subsystems that use corruptible metadata for memory management, such as linked lists.

Attackers leverage memory corruption vulnerabilities to establish primitives for reading from or writing to the address space of a vulnerable process. These primitives form the foundation for code-reuse and data-oriented attacks. While various defenses against the former class of attacks have proven effective, mitigation of the latter remains an open problem. In this paper, we identify various shortcomings of the x86 architecture regarding memory isolation, and leverage virtualization to build an effective defense against data-oriented attacks. Our approach, called xMP, provides (in-guest) selective memory protection primitives that allow VMs to isolate sensitive data in user or kernel space in disjoint xMP domains. We interface the Xen altp2m subsystem with the Linux memory management system, lending VMs the flexibility to define custom policies. Contrary to conventional approaches, xMP takes advantage of virtualization extensions, but after initialization, it does not require any hypervisor intervention. To ensure the integrity of in-kernel management information and pointers to sensitive data within isolated domains, xMP protects pointers with HMACs bound to an immutable context, so that integrity validation succeeds only in the right context. We have applied xMP to protect the page tables and process credentials of the Linux kernel, as well as sensitive data in various user-space applications. Overall, our evaluation shows that xMP introduces minimal overhead for real-world workloads and applications, and offers effective protection against data-oriented attacks.

ARM has become the leading processor architecture for mobile and IoT devices, while it has recently started claiming a bigger slice of the server market pie as well. As such, it will not be long before malware more regularly target the ARM architecture. Therefore, the stealthy operation of Virtual Machine Introspection (VMI) is an obligation to successfully analyze and proactively mitigate this growing threat. Stealthy VMI has proven itself perfectly suitable for malware analysis on Intel's architecture, yet, it often lacks the foundation required to be equally effective on ARM.