Replay and rollback attacks threaten cloud application integrity by reintroducing authentic yet stale data through an untrusted storage interface to compromise application decision-making. Prior security frameworks mitigate these attacks by enforcing forward-only state transitions (state continuity) with hardware-backed mechanisms, but they categorically treat all rollback as malicious and thus preclude legitimate rollbacks used for operational recovery from corruption or misconfiguration. We present Rebound, a general-purpose security framework that preserves rollback protection while enabling policy-authorized legitimate rollbacks of application binaries, configuration, and data. Key to Rebound is a reference monitor that mediates state transitions, enforces authorization policy, guarantees atomicity of state updates and rollbacks, and emits a tamper-evident log that provides transparency to applications and auditors. We analyze Rebound's security properties and show through an application case study -- with software deployment workflows in GitLab CI -- that it enables robust control over binary, configuration, and raw data versioning with low end-to-end overhead.

While machine learning has been adopted across various fields, its ability to outperform traditional heuristics in operating systems is often met with justified skepticism. Concerns about unsafe decisions, opaque debugging processes, and the challenges of integrating ML into the kernel---given its stringent latency constraints and inherent complexity --- make practitioners understandably cautious. This paper introduces Guardrails for the OS, a framework that allows kernel developers to declaratively specify system-level properties and define corrective actions to address property violations. The framework facilitates the compilation of these guardrails into monitors capable of running within the kernel. In this work, we establish the foundation for Guardrails, detailing its core abstractions, examining the problem space, and exploring potential solutions.

Hardware capabilities offer an alternative to how access control is commonly implemented in processors today, i.e., through enforcement of permission checks on virtual memory at the time of address translation. Despite conceptual strengths of capability hardware, it is challenging for existing capability-oblivious software stacks to be compatible with capability hardware and to interoperate with capability-aware software, hindering faster adoption. Prior attempts to achieve this sacrifice the inherent advantages of capabilities. They require trusting a software central authority (e.g., the OS kernel) for capability-based isolation and limit the scope of capability-based memory sharing to individual virtual address spaces. This paper proposes the idea of caplification, a novel mechanism to enable seamless co-existence of capability-aware and capability-oblivious software stacks. We concretely implement our proposed idea on a modern RISC-V capability hardware and show how it enables running a commodity unmodified (or capability-oblivious) Linux OS. Our design retains the full advantages provided by hardware capabilities, such as creating fine-grained hardware-isolated memory compartments both in user- and kernel-space. We evaluated our prototype system both on QEMU emulation and on hardware RTL simulation. We find that the performance of our system is comparable to prior baseline designs, while offering cost improvements in scenarios of secure data sharing.

Storage integrity is essential to systems and applications that use untrusted storage (e.g., public clouds, end-user devices). However, known methods for achieving storage integrity either suffer from high (and often prohibitive) overheads or provide weak integrity guarantees. In this work, we demonstrate a hybrid approach to storage integrity that simultaneously reduces overhead while providing strong integrity guarantees. Our system, partially asynchronous integrity checking (PAC), allows disk write commitments to be deferred while still providing guarantees around read integrity. PAC delivers a 5.5 × throughput and latency improvement over the state of the art, and 85% of the throughput achieved by non-integrity-assuring approaches. In this way, we show that untrusted storage can be used for integrity-critical workloads without meaningfully sacrificing performance.