Related papers: CAEC: Confidential, Attestable, and Efficient Inte…

Agentic AI systems, specifically LLM-driven agents that plan, invoke tools, maintain persistent memory, and delegate tasks to peer agents via protocols such as MCP and A2A, introduce a threat surface that differs materially from standalone…

Confidential Virtual Machines (CVMs) are a type of VMbased Trusted Execution Environments (TEEs) designed to enhance the security of cloud-based VMs, safeguarding them even from malicious hypervisors. Although CVMs have been widely adopted…

Multi-tenant computing platforms are typically comprised of several software and hardware components including platform firmware, host operating system kernel, virtualization monitor, and the actual tenant payloads that run on them…

Confidential computing safeguards sensitive computations from untrusted clouds, with Confidential Virtual Machines (CVMs) providing a secure environment for guest OS. However, CVMs often come with large and vulnerable operating system…

In this work we present the Secure Machine, SeM for short, a CPU architecture extension for secure computing. SeM uses a small amount of in-chip additional hardware that monitors key communication channels inside the CPU chip, and only acts…

Machine learning benefits from large training datasets, which may not always be possible to collect by any single entity, especially when using privacy-sensitive data. In many contexts, such as healthcare and finance, separate parties may…

AI agents powered by large language models are increasingly deployed as cloud services that autonomously access sensitive data, invoke external tools, and interact with other agents. However, these agents run within a complex multi-party…

Confidential Computing (CC) has received increasing attention in recent years as a mechanism to protect user data from untrusted operating systems (OSes). Existing CC solutions hide confidential memory from the OS and/or encrypt it to…

Modern computing systems are limited in performance by the memory bandwidth available to processors, a problem known as the memory wall. Processing-in-Memory (PIM) promises to substantially improve this problem by moving processing closer…

Virtual memory is an essential mechanism for enforcing security boundaries, but its relaxed-memory concurrency semantics has not previously been investigated in detail. The concurrent systems code managing virtual memory has been left on an…

The rapid advancement of cloud-native technologies has created an urgent need for security. Currently, confidential containers are increasingly deployed in multi-tenant environments. Existing confidential container designs mainly adopt a…

Virtualization has become more important since cloud computing is getting more and more popular than before. There is an increasing demand for security among the cloud customers. AMD plans to provide Secure Encrypted Virtualization (SEV)…

Customers of cloud services have to trust the cloud providers, as they control the building blocks that form the cloud. This includes the hypervisor enabling the sharing of a single hardware platform among multiple tenants. AMD Secure…

Confidentiality, integrity protection, and high availability, abbreviated to CIA, are essential properties for trustworthy data systems. The rise of cloud computing and the growing demand for multiparty applications however means that…

This paper presents an innovative approach utilizing in-memory computing (IMC) for the development and integration of AES (Advanced Encryption Standard) cipher technique. Our research aims to enhance cybersecurity measures for a wide range…

Confidential computing in the public cloud intends to safeguard workload privacy while outsourcing infrastructure management to a cloud provider. This is achieved by executing customer workloads within so called Trusted Execution…

Computing-in-Memory (CiM) architectures aim to reduce costly data transfers by performing arithmetic and logic operations in memory and hence relieve the pressure due to the memory wall. However, determining whether a given workload can…

Cloud workloads have dominated generative AI based on large language models (LLM). Specialized hardware accelerators, such as GPUs, NPUs, and TPUs, play a key role in AI adoption due to their superior performance over general-purpose CPUs.…

Getting the best performance from the ever-increasing number of hardware platforms has been a recurring challenge for data processing systems. In recent years, the advent of data science with its increasingly numerous and complex types of…

Hardware-based Trusted execution environments (TEEs) offer an isolation granularity of virtual machine abstraction. They provide confidential VMs (CVMs) that host security-sensitive code and data. AMD SEV-SNP and Intel TDX enable CVMs and…