Hypervisors have played a critical role in cloud security, but
they introduce a large trusted computing base (TCB) and
incur a heavy performance tax. As of late, hypervisor offloading
has become an emerging trend, where privileged
functions are sunk into specially-designed hardware devices
(e.g., Amazon’s Nitro, AMD’s Pensando) for better security
with closer-to-baremetal performance.
In light of this trend, this project rearchitects a classic security
task that is often relegated to the hypervisor, memory
introspection, while only using widely-available devices.
Remote direct memory introspection (RDMI) couples two
types of commodity programmable devices in a novel defense
platform. It uses RDMA NICs for efficient memory access
and programmable network devices for efficient computation,
both operating at ASIC speeds. RDMI also provides a
declarative language for users to articulate the introspection
task, and its compiler automatically lowers the task to the
hardware substrate for execution. Our evaluation shows that
RDMI can protect baremetal machines without requiring a
hypervisor, introspecting kernel state and detecting rootkits
at high frequency and zero CPU overhead.
more »
« less
Remote Direct Memory Introspection
Hypervisors have played a critical role in cloud security, but they introduce a large trusted computing base (TCB) and incur a heavy performance tax. As of late, hypervisor of- floading has become an emerging trend, where privileged functions are sunk into specially-designed hardware devices (e.g., Amazon’s Nitro, AMD’s Pensando) for better security with closer-to-baremetal performance.
In light of this trend, this project rearchitects a classic security task that is often relegated to the hypervisor, mem- ory introspection, while only using widely-available devices. Remote direct memory introspection (RDMI) couples two types of commodity programmable devices in a novel defense platform. It uses RDMA NICs for efficient memory access and programmable network devices for efficient computa- tion, both operating at ASIC speeds. RDMI also provides a declarative language for users to articulate the introspection task, and its compiler automatically lowers the task to the hardware substrate for execution. Our evaluation shows that RDMI can protect baremetal machines without requiring a hypervisor, introspecting kernel state and detecting rootkits at high frequency and zero CPU overhead.
more »
« less
- Award ID(s):
- 2115587
- PAR ID:
- 10524160
- Publisher / Repository:
- Usenix Security 2023
- Date Published:
- ISBN:
- 978-1-939133-37-3
- Format(s):
- Medium: X
- Location:
- https://dl.acm.org/doi/10.5555/3620237.3620575
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Hypervisors have played a critical role in cloud security, but they introduce a large trusted computing base (TCB) and incur a heavy performance tax. As of late, hypervisor offloading has become an emerging trend, where privileged functions are sunk into specially-designed hardware devices (e.g., Amazon’s Nitro, AMD’s Pensando) for better security with closer-to-baremetal performance. In light of this trend, this project rearchitects a classic security task that is often relegated to the hypervisor, memory introspection, while only using widely-available devices. Remote direct memory introspection (RDMI) couples two types of commodity programmable devices in a novel defense platform. It uses RDMA NICs for efficient memory access and programmable network devices for efficient computation, both operating at ASIC speeds. RDMI also provides a declarative language for users to articulate the introspection task, and its compiler automatically lowers the task to the hardware substrate for execution. Our evaluation shows that RDMI can protect baremetal machines without requiring a hypervisor, introspecting kernel state and detecting rootkits at high frequency and zero CPU overhead.more » « less
-
In cloud-native environments, containers are often deployed within lightweight virtual machines (VMs) to ensure strong security isolation and privacy protection. With the growing demand for customized cloud services, third-party vendors are turning to infrastructure-as-a-service (IaaS) cloud providers to build their own cloud-native platforms, necessitating the need to run a VM or a guest that hosts containers inside another VM instance leased from an IaaS cloud. State-of-the-art nested virtualization in the x86 architecture relies heavily on the host hypervisor to expose hardware virtualization support to the guest hypervisor, not only complicating cloud management but also raising concerns about an increased attack surface at the host hypervisor. This paper presents the design and implementation of PVM, a high-performance guest hypervisor for KVM that is transparent to the host hypervisor and assumes no hardware virtualization support. PVM leverages two key designs: 1) a minimal shared memory region between the guest and guest hypervisor to facilitate state transition between different privilege levels and 2) an efficient shadow page table design to reduce the cost of memory virtualization. PVM has been adopted by a major IaaS cloud provider for hosting tens of thousands of secure containers on a daily basis. Our experiments demonstrate that PVM significantly outperforms current nested virtualization in KVM for memory virtualization, particularly for concurrent workloads, while maintaining comparable performance in CPU and I/O virtualization.more » « less
-
Hypervisors are widely deployed by cloud computing providers to support virtual machines, but their growing complexity poses a security risk, as large codebases contain many vulnerabilities. We present SeKVM, a layered Linux KVM hypervisor architecture that has been formally verified on multiprocessor hardware. Using layers, we isolate KVM's trusted computing base into a small core such that only the core needs to be verified to ensure KVM's security guarantees. Using layers, we model hardware features at different levels of abstraction tailored to each layer of software. Lower hypervisor layers that configure and control hardware are verified using a novel machine model that includes multiprocessor memory management hardware such as multi-level shared page tables, tagged TLBs, and a coherent cache hierarchy with cache bypass support. Higher hypervisor layers that build on the lower layers are then verified using a more abstract and simplified model, taking advantage of layer encapsulation to reduce proof burden. Furthermore, layers provide modularity to reduce verification effort across multiple implementation versions. We have retrofitted and verified multiple versions of KVM on Arm multiprocessor hardware, proving the correctness of the implementations and that they contain no vulnerabilities that can affect KVM's security guarantees. Our work is the first machine-checked proof for a commodity hypervisor using multiprocessor memory management hardware. SeKVM requires only modest KVM modifications and incurs only modest performance overhead versus unmodified KVM on real application workloads.more » « less
-
This paper outlines the vision for a new type of software-shaped platforms, or SOSH platforms for short, that can be implemented in commercial CPU+FPGA platforms. At the core of the SOSH paradigm is the idea of exposing direct control over the flow of data exchanged between hardware components in embedded Systemon-Chips (SoC). Data flow manipulation primitives are synthesized in reprogrammable hardware and interposed between central processors, memory modules, and I/O devices. A new layer of system software is then introduced to leverage such primitives and to achieve fine-grained control and introspection over the interaction of SoC resources. By turning memory and I/O data flows into manageable entities, a new degree of internal awareness can be achieved in complex systems. We first review recent works that are well aligned with the concept of data flow manipulation primitives that can be deployed in SOSH platforms. Next, we outline future research avenues concerning the use of the SOSH paradigm for workload profiling and prediction, to implement advanced memory models, and to perform security threat identification and mitigation.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Proceeding:
- The DOI is not currently available.
