Memory Protection Keys for Userspace (PKU) is a recent hardware feature that allows programs to assign virtual memory pages to protection domains, and to change domain access permissions using inexpensive, unprivileged instructions. Several in-process memory isolation approaches leverage this feature to prevent untrusted code from accessing sensitive program state and data. Typically, PKU-based isolation schemes need to be used in conjunction with mitigations such as CFI because untrusted code, when compromised, can otherwise bypass the PKU access permissions using unprivileged instructions or operating system APIs. Recently, researchers proposed fully self-contained PKU-based memory isolation schemes that do not rely on other mitigations. These systems use exploit-proof call gates to transfer control between trusted and untrusted code, as well as a sandbox that prevents tampering with the PKU infrastructure from untrusted code. In this paper, we show that these solutions are not complete. We first develop two proof-of-concept attacks against a state-of-the-art PKU-based memory isolation scheme. We then present Cerberus, a PKU-based sandboxing framework that can overcome limitations of existing sandboxes. We apply Cerberus to several memory isolation schemes, and show that it is practical, efficient, and secure.

Fitness tracking social networks such as Strava allow users to record sports activities and share them publicly. Sharing encourages peer interaction but also constitutes a risk, because an activity's start or finish may inadvertently reveal privacy-sensitive locations such as a home or workplace. To mitigate this risk, networks introduced endpoint privacy zones (EPZs), which hide track portions around protected locations. In this paper, we show that EPZ implementations of major services remain vulnerable to inference attacks that significantly reduce the effective anonymity provided by the EPZ, and even reveal the protected location. Our attack leverages distance information leaked in activity metadata, street grid data, and the locations of the entry points into the EPZ. This yields a constrained search space where we use regression analysis to predict protected locations. Our evaluation on 1.4 million Strava activities shows that our attack discovers the protected location for up to 85% of EPZs. Larger EPZs reduce the performance of our attack, while geographically dispersed activities in sparser street grids yield better performance. We propose six countermeasures, that, however, come with a usability trade-off, and responsibly disclosed our findings and countermeasures to the major networks.

Multi-Variant Execution Environments (MVEEs) are a powerful tool for protecting legacy software against memory corruption attacks. MVEEs employ software diversity to run multiple variants of the same program in lockstep, whilst providing them with the same inputs and comparing their behavior. Well-constructed variants will behave equivalently under normal operating conditions but diverge when under attack. The MVEE detects these divergences and takes action before compromised variants can damage the host system. Existing MVEEs replicate inputs at the system call boundary, and therefore do not support programs that use shared-memory IPC with other processes, since shared memory pages can be read from and written to directly without system calls. We analyzed modern applications, ranging from web servers, over media players, to browsers, and observe that they rely heavily on shared memory, in some cases for their basic functioning and in other cases for enabling more advanced functionality. It follows that modern applications cannot enjoy the security provided by MVEEs unless those MVEEs support shared-memory IPC. This paper first identifies the requirements for supporting shared-memory IPC in an MVEE. We propose a design that involves techniques to identify and instrument accesses to shared memory pages, as well as techniques to replicate I/O through shared-memory IPC. We implemented these techniques in a prototype MVEE and report our findings through an evaluation of a range of benchmark programs. Our contributions enable the use of MVEEs on a far wider range of programs than previously supported. By overcoming one of the major remaining limitations of MVEEs, our contributions can help to bolster their real-world adoption.

Multi-variant execution (MVX) systems amplify the effectiveness of software diversity techniques. The key idea is to run multiple diversified program variants in lockstep while providing them with the same input and monitoring their run-time behavior for divergences. Thus, adversaries have to compromise all program variants simultaneously to mount an attack successfully. Recent work proposed distributed, heterogeneous MVX systems that leverage different ABIs and ISAs to increase the diversity between program variants further. However, existing distributed MVX system designs suffer from high performance overhead due to time-consuming network transactions required for the MVX operations. This paper presents dMVX, a novel hybrid distributed MVX design, which incorporates new techniques that significantly reduce the overhead of MVX systems in a distributed setting. Our key insight is that we can intelligently reduce the MVX operations that use expensive network transfers. First, we can limit the monitoring of system calls that are not security-critical. Second, we observe that, in many circumstances, we can also safely avoid replication operations needed for I/O related system calls. Our evaluation shows that dMVX reduces the performance degradation from over 50% to 3.1% for realistic server benchmarks.

N-Variant Execution (NVX) systems utilize artificial diversity techniques to enhance software security. The general idea is to run multiple different variants of the same program alongside each other while monitoring their diverging behavior on a malicious input. Existing NVX systems execute diversified program variants on a single host. This means the level of inter-variant diversity will be limited to what a single platform can offer, without costly emulation. This paper presents DMON, a novel distributed NVX design that executes native program variants across multiple heterogeneous hosts. Our approach greatly increases the level of diversity between the simultaneously running variants that can be supported, encompassing different ISAs and ABIs. Our evaluation shows that DMON can provide comparable performance to traditional, non-distributed NVX systems, while enhancing security.