ACE: Confidential Computing for Embedded RISC-V Systems

• URL: http://arxiv.org/abs/2505.12995v1
• Date: Mon, 19 May 2025 11:32:43 GMT
• Title: ACE: Confidential Computing for Embedded RISC-V Systems
• Abstract summary: We introduce the Assured Confidential Execution (ACE), an open-source and royalty-free confidential computing technology.<n>We present a set of principles and a methodology that we used to build ACE and that might be applied for developing other embedded systems.
• Score: 1.545844275209198
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Confidential computing plays an important role in isolating sensitive applications from the vast amount of untrusted code commonly found in the modern cloud. We argue that it can also be leveraged to build safer and more secure mission-critical embedded systems. In this paper, we introduce the Assured Confidential Execution (ACE), an open-source and royalty-free confidential computing technology targeted for embedded RISC-V systems. We present a set of principles and a methodology that we used to build \ACE and that might be applied for developing other embedded systems that require formal verification. An evaluation of our prototype on the first available RISC-V hardware supporting virtualization indicates that ACE is a viable candidate for our target systems.

Related papers

• Toward Comprehensive Risk Assessments and Assurance of AI-Based Systems [0.0]
  Safety, socio-economic, and ethical harms arising from the deployment of AI-based systems have led to a breadth of work seeking to map, measure, and mitigate against newly found risks.<n>Previous attempts to adapt risk assessment techniques and terminology from the safety and security fields have been insufficient.<n>We propose a novel end-to-end AI risk framework that integrates the concept of an Operational Design Domains (ODD) for more general AI-based systems.
  arXiv  Detail & Related papers  (2026-06-28T13:26:03Z)
• Formal Verification of Secure Encrypted Virtualization [2.595331128138709]
  AMD secure encrypted virtualization (SEV) is a key technology used in confidential computing in the cloud.<n>Our research introduces a formal framework for representing and verifying AMD SEV confidential and integrity.<n>This approach provides a rigorous foundation for defining and verifying key security attributes for safeguarding execution environments.
  arXiv  Detail & Related papers  (2026-05-31T18:10:47Z)
• A Survey on Reconfigurable Intelligent Surfaces in Practical Systems: Security and Privacy Perspectives [35.265580543159835]
  Reconfigurable Intelligent Surfaces (RIS) have emerged as transformative technology capable of reshaping wireless environments through dynamic manipulation of electromagnetic waves.<n>This survey provides a comprehensive examination of RIS applications in real-world systems, with a focus on the security and privacy threats, vulnerabilities, and defensive strategies relevant to practical use.
  arXiv  Detail & Related papers  (2025-12-12T00:54:24Z)
• OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows [77.95511352806261]
  Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.<n>We propose OS-Sentinel, a novel hybrid safety detection framework that combines a Formal Verifier for detecting explicit system-level violations with a Contextual Judge for assessing contextual risks and agent actions.
  arXiv  Detail & Related papers  (2025-10-28T13:22:39Z)
• Qualcomm Trusted Application Emulation for Fuzzing Testing [0.3277163122167433]
  This research centers on trusted applications (TAs) within the Qualcomm TEE.<n>Through reverse engineering techniques, we develop a partial emulation environment that accurately emulates their behavior.<n>We integrate fuzzing testing techniques into the emulator to systematically uncover potential vulnerabilities within Qualcomm TAs.
  arXiv  Detail & Related papers  (2025-07-11T06:10:15Z)
• Enabling Security on the Edge: A CHERI Compartmentalized Network Stack [42.78181795494584]
  CHERI provides strong security from the hardware level by enabling fine-grained compartmentalization and memory protection.<n>Our case study examines the trade-offs of isolating applications, TCP/IP libraries, and network drivers on a CheriBSD system deployed on the Arm Morello platform.
  arXiv  Detail & Related papers  (2025-07-07T09:37:59Z)
• LLM Agents Should Employ Security Principles [60.03651084139836]
  This paper argues that the well-established design principles in information security should be employed when deploying Large Language Model (LLM) agents at scale.<n>We introduce AgentSandbox, a conceptual framework embedding these security principles to provide safeguards throughout an agent's life-cycle.
  arXiv  Detail & Related papers  (2025-05-29T21:39:08Z)
• Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things [61.43014629640404]
  Zero-Trust Foundation Models (ZTFMs) embed zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems.<n>ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments.
  arXiv  Detail & Related papers  (2025-05-26T06:44:31Z)
• A Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron? [31.057071280437796]
  We present a systematization of knowledge on the safety and security threats of emphComputer-Using Agents.<n> CUAs are capable of autonomously performing tasks such as navigating desktop applications, web pages, and mobile apps.
  arXiv  Detail & Related papers  (2025-05-16T06:56:42Z)
• Privacy-Aware RAG: Secure and Isolated Knowledge Retrieval [7.412110686946628]
  This paper proposes an advanced encryption methodology designed to protect RAG systems from unauthorized access and data leakage.<n>Our approach encrypts both textual content and its corresponding embeddings prior to storage, ensuring that all data remains securely encrypted.<n>Our findings suggest that integrating advanced encryption techniques into the design and deployment of RAG systems can effectively enhance privacy safeguards.
  arXiv  Detail & Related papers  (2025-03-17T07:45:05Z)
• T-Edge: Trusted Heterogeneous Edge Computing [11.859343440194944]
  This paper proposes a practical trusted execution environment design for ARM/FPGA System-on-Chip platforms.<n>The design features a dedicated security controller within the ARM TrustZone, overseeing FPGA reconfiguration and managing communication between CPU cores and FPGA fabrics.<n>We employ an automated protocol verifier, ProVerif, to validate its compliance with essential security requirements.
  arXiv  Detail & Related papers  (2024-12-18T14:45:07Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• Securing the Open RAN Infrastructure: Exploring Vulnerabilities in Kubernetes Deployments [60.51751612363882]
  We investigate the security implications of and software-based Open Radio Access Network (RAN) systems. We highlight the presence of potential vulnerabilities and misconfigurations in the infrastructure supporting the Near Real-Time RAN Controller (RIC) cluster.
  arXiv  Detail & Related papers  (2024-05-03T07:18:45Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• Fortress: Securing IoT Peripherals with Trusted Execution Environments [2.2476099815732518]
  Internet of Things (IoT) devices often collect confidential information, such as audio and visual data, through peripheral inputs like microphones and cameras. We propose a generic design to enhance the privacy in IoT-based systems by isolating peripheral I/O memory regions in a secure kernel space of a trusted execution environment (TEE) The sensitive peripheral data is then securely transferred to a user-space TEE, where obfuscation mechanisms can be applied before it is relayed to third parties, e.g., the cloud.
  arXiv  Detail & Related papers  (2023-12-05T07:12:58Z)
• A Holistic Approach for Trustworthy Distributed Systems with WebAssembly and TEEs [2.0198678236144474]
  This paper introduces a novel approach using WebAssembly to address these issues. We present the design of a portable and fully attested publish/subscribe system as a holistic approach. Our experimental results showcase most overheads, revealing a 1.55x decrease in message throughput when using a trusted broker.
  arXiv  Detail & Related papers  (2023-12-01T16:37:48Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.