Trusted execution environments (TEE) represent a major technological breakthrough that provide strong confidentiality and integrity guarantees for code and data running on potentially vulnerable or untrustworthy computing systems, such as cloud, edge, embedded, mobile, or even blockchain systems. However, the performance overhead associated with TEEs still poses a limitation on the extent to which real-time (RT) sensitive applications can benefit from this technology, e.g., to run on untrusted third-party infrastructures. This work investigates various TEE-based architectures spanning from process-based to virtual-machine-based implementations, for securing RT applications. It offers in addition an in-depth evaluation of these architectures, providing insights into how various TEE deployments influence the temporal compute and communication guarantees of RT systems.

Threat models contribute to strengthen the security of an enterprise system by listing the cybersecurity threats that might affect it as well as possible mitigations to such threats. Given the importance of these models, different tools have been devised to support creating or updating threat models. However, these tools focus mainly on the needs of cybersecurity experts, often overlooking non-expert users.To address this gap, in the first part of this paper we present a chat-based LLM assistant to answer both expert and non-expert queries on threat models. The assistant is based on a microservice architecture and uses Retrieval-Augmented Generation (RAG) to extract relevant information from an industrial threat model.Guaranteeing the reliability of an LLM’s answers in sensitive fields such as cybersecurity is paramount. For this reason, in the second part of this paper, we evaluate the LLM assistant using (i) Bleu and Rouge score metrics, (ii) human evaluation, and (iii) an automatic LLM-as-a-judge approach.The evaluation, conducted on 275 test cases, confirms the accuracy of the answers provided by our LLM-based threat model assistant. Additionally, the results of the human and LLM-as-a-judge evaluations are consistent. This corroborates the effectiveness of automatic LLM-as-a-judge methods in assessing the performance of LLM-based industrial solutions.

Validation of conformance to cybersecurity standards for industrial automation and control systems is an expensive and time consuming process which can delay the time to market. It is therefore crucial to introduce conformance validation stages into the continuous integration/continuous delivery pipeline of products. However, designing such conformance validation in an automated fashion is a highly non-trivial task that requires expert knowledge and depends upon available security tools, ease of integration into the DevOps pipeline, as well as support for IT and OT interfaces and protocols.This paper addresses the aforementioned problem focusing on the automated validation of ISA/IEC 62443-4-2 standard component requirements. We present an extensive qualitative analysis of the standard requirements and the current tooling landscape to perform validation. Our analysis demonstrates the coverage established by the currently available tools and sheds light on current gaps to achieve full automation and coverage. Furthermore, we showcase for every component requirement where in the CI/CD pipeline stage it is recommended to test it and the tools to do so.

Attestation is a fundamental building block to establish trust over software systems. When used in conjunction with trusted execution environments, it guarantees the genuineness of the code executed against powerful attackers and threats, paving the way for adoption in several sensitive application domains. This paper reviews remote attestation principles and explains how the modern and industrially well-established trusted execution environments Intel SGX, Arm TrustZone and AMD SEV, as well as emerging RISC-V solutions, leverage these mechanisms.

Latest ARM processors are approaching the computational power of x86 architectures while consuming much less energy. Consequently, supply follows demand with Amazon EC2, Equinix Metal and Microsoft Azure offering ARM-based instances, while Oracle Cloud Infrastructure is about to add such support. We expect this trend to continue, with an increasing number of cloud providers offering ARM-based cloud instances. ARM processors are more energy-efficient leading to substantial electricity savings for cloud providers. However, a malicious cloud provider could intentionally reduce the CPU voltage to further lower its costs. Running applications malfunction when the undervolting goes below critical thresholds. By avoiding critical voltage regions, a cloud provider can run undervolted instances in a stealthy manner. This practical experience report describes a novel attack scenario: an attack launched by the cloud provider against its users to aggressively reduce the processor voltage for saving energy to the last penny. We call it the Scrooge Attack and show how it could be executed using ARM-based computing instances. We mimic ARM-based cloud instances by deploying our own ARM-based devices using different generations of Raspberry Pi. Using realistic and synthetic workloads, we demonstrate to which degree of aggressiveness the attack is relevant. The attack is unnoticeable by our detection method up to an offset of −50 mV. We show that the attack may even remain completely stealthy for certain workloads. Finally, we propose a set of client-based detection methods that can identify undervolted instances. We support experimental reproducibility and provide instructions to reproduce our results.

The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC, balanced with the security and resilience challenges. LEGaTO is an ongoing three-year EU H2020 project started in December 2017.

Blockchain technology promises to revolutionize manufacturing industries. For example, several supply chain use cases may benefit from transparent asset tracking and automated processes using smart contracts. Several real-world deployments exist where the transparency aspect of a blockchain is both an advantage and a disadvantage at the same time. The exposure of assets and business interaction represent critical risks. However, there are typically no confidentiality guarantees to protect the smart contract logic as well as the processed data. Trusted execution environments (TEE) are an emerging technology available in both edge or mobile-grade processors (e.g., ARM TrustZone) and server-grade processors (e.g., Intel SGX). TEEs shield both code and data from malicious attackers. This practical experience report presents TZ4FABRIC, an extension of Hyperledger Fabric to leverage ARM TrustZone for the secure execution of smart contracts. Our design minimizes the trusted computing base executed by avoiding the execution of a whole Hyperledger Fabric node inside the TEE, which continues to run in untrusted environment. Instead, we restrict it to the execution of only the smart contract. The TZ4FABRIC prototype exploits the opensource OP-TEE framework, as it supports deployments on cheap low-end devices (e.g., Raspberry Pis). Our experimental results highlight the performance trade-off due to the additional security guarantees provided by ARM TrustZone. TZ4FABRIC will be released as open source.

Cloud providers usually offer diverse types of hardware for their users. Customers exploit this option to deploy cloud instances featuring GPUs, FPGAs, architectures other than x86 (e.g., ARM, IBM Power8), or featuring certain specific extensions (e.g., Intel SGX). We consider in this work the instances used by customers to deploy containers, nowadays the de facto standard for micro-services, or to execute computing tasks. In doing so, the underlying container orchestrator (e.g., Kubernetes) should be designed so as to take into account and exploit this hardware diversity. In addition, besides the feature range provided by different machines, there is an often overlooked diversity in the energy requirements introduced by hardware heterogeneity, which is simply ignored by default container orchestrator's placement strategies. We introduce Heats, a new task-oriented and energy-aware orchestrator for containerized applications targeting heterogeneous clusters. Heats allows customers to trade performance vs. energy requirements. Our system first learns the performance and energy features of the physical hosts. Then, it monitors the execution of tasks on the hosts and opportunistically migrates them onto different cluster nodes to match the customer-required deployment trade-offs. Our Heats prototype is implemented within Google's Kubernetes. The evaluation with synthetic traces in our cluster indicate that our approach can yield considerable energy savings (up to 8.5%) and only marginally affect the overall runtime of deployed tasks (by at most 7%). Heats is released as open-source.

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.

The deployment of large-scale distributed systems, e.g., publish-subscribe platforms, that operate over sensitive data using the infrastructure of public cloud providers, is nowadays heavily hindered by the surging lack of trust toward the cloud operators. Although purely software-based solutions exist to protect the confidentiality of data and the processing itself, such as homomorphic encryption schemes, their performance is far from being practical under real-world workloads. The performance trade-offs of two novel hardware-assisted memory protection mechanisms, namely AMD SEV and Intel SGX - currently available on the market to tackle this problem, are ADD described in this practical experience. Specifically, we implement and evaluate a publish/subscribe use-case and evaluate the impact of the memory protection mechanisms and the resulting performance. This paper reports on the experience gained while building this system, in particular when having to cope with the technical limitations imposed by SEV and SGX. Several tradeoffs that provide valuable insights in terms of latency, throughput, processing time and energy requirements are exhibited by means of micro-and macro-benchmarks.

Major cloud providers such as Amazon [1], Google [2] and Microsoft [3] provide nowadays some form of infrastructure as a service (IaaS) which allows deploying services in the form of virtual machines [4], containers [5] or bare-metal [6] instances. Although software-based solutions like homomorphic encryption exit, privacy concerns [7] greatly hinder the deployment of such services over public clouds. It is particularly difficult for homomorphic encryption to match performance requirements of modern workloads [8]. Evaluating simple operations on basic data types with HElib [9], a homomorphic encryption library, against their unencrypted counter part reveals, that homomorphic encryption is still impractical under realistic workloads.