Backend
CVM-Store: A Secure Storage System for Confidential Virtual Machines
arXiv cs.CR0 views
arXiv:2610.34560v1 Announce Type: new Abstract: Stateful cloud workloads require high-performance persistent storage when running on untrusted cloud infrastructure. Confidential Virtual Machines (CVMs) promise to protect these workloads through hardware-enforced memory encryption and isolation without requiring application changes. However, they protect only data in use; persistent storage lies outside the CVM trust boundary, exposing all stateful data to the cloud provider. Extending the trust boundary to persistent storage presents inherent challenges. High-bandwidth workloads require near-raw NVMe throughput, yet the default kernel CVM I/O stacks are inadequate, and userspace I/O libraries are not designed with CVM use cases in mind. Access patterns and storage semantics vary between workloads, necessitating access-pattern-aware security abstractions rather than uniform block-level encryption. In order to mitigate against rollback, freshness is needed, but CVMs lack hardware primitives for this purpose, requiring alternative solutions. We present CVM-Store, a storage system that co-designs a high-throughput userspace I/O engine with CIF (confidentiality, integrity, freshness) data protection. The key insight is that neither authenticated encryption nor I/O is a bottleneck by itself, but their naive combination is. CVM-Store exploits this by interleaving them, eliminating redundant copies and OS/hypervisor mediation. CIF guarantees are provided as composable primitives that adapt protection to workload access patterns, from which we derive three concrete authenticated abstractions: a KVS, a log, and a filesystem. An asynchronous trusted counter service provides freshness guarantees with epoch-based batching, which enables rollback protection without dedicated hardware support. CVM-Store delivers full CIF guarantees within $\approx$1% (write) and 1-19% (read) of raw NVMe bandwidth for large objects.
Source:https://arxiv.org/abs/2610.34560PreviousLLM WikiLeaks: Uncovering URL-Based Capability Extension and Security Circumvention in Agentic Message BoardsNextFrom Representations to Computational Trajectories: Intrinsic Fingerprinting for Large Language Model Provenance Verification
Related posts
Turn Today’s Messy Notes Into Tomorrow’s Checklist With AI
0dev.to AI
A 763B MoE at "2 Bits": Where the Bytes Really Go in the GSQ-RCO GGUFs, and Why 100% Recovery Sits Inside the Noise
0dev.to AI
How I ship fixes to open-source projects maintainers actually merge (with AI in the loop)
0dev.to AI
Prompt Injection Through Documents: How to Harden an LLM Extraction Pipeline
0dev.to AI
Keeping noindex, the sitemap and the image sitemap in agreement for programmatic pages
0dev.to AI
3 Things in AI You Should Know: Character.AI Files for Bankruptcy
0dev.to AI