Related papers: A Last-Level Defense for Application Integrity and…

With the increasing deployment of Large Language Models (LLMs) on mobile and edge platforms, securing them against model extraction attacks has become a pressing concern. However, protecting model privacy without sacrificing the performance…

Protecting sensitive information in data-driven collaborations, such as AI training, while meeting the diverse requirements of multiple mutually distrusted stakeholders, is both crucial and challenging. This paper presents Styx, a novel…

Confidential Virtual Machines (CVMs) provide isolation guarantees for data in use, but their threat model does not include physical level protection and side-channel attacks. Therefore, current deployments rely on trusted cloud providers to…

Despite their potential of increasing operational efficiency, transparency, and safety, the use of Localization and Tracking Systems (LTSs) in warehouse environments remains seldom. One reason is the lack of market transparency and…

The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor industry. While LLMs offer unprecedented capabilities in generating Register Transfer Level…

Trusted Execution Environments (TEEs) such as ARM TrustZone are widely used in IoT and embedded devices to protect sensitive code and data. However, most existing defenses focus on secure boot or REE-side monitoring and provide little…

Technology is shaping our lives in a multitude of ways. This is fuelled by a technology infrastructure, both legacy and state of the art, composed of a heterogeneous group of hardware, software, services and organisations. Such…

Large Language Models (LLMs) have achieved remarkable success but remain highly susceptible to jailbreak attacks, in which adversarial prompts coerce models into generating harmful, unethical, or policy-violating outputs. Such attacks pose…

Security-critical tasks require proper isolation from untrusted software. Chip manufacturers design and include trusted execution environments (TEEs) in their processors to secure these tasks. The integrity and security of the software in…

Threat modelling is foundational to secure systems engineering and should be done in consideration of the context within which systems operate. On the other hand, the continuous evolution of both the technical sophistication of threats and…

To safeguard user data privacy, on-device inference has emerged as a prominent paradigm on mobile and Internet of Things (IoT) devices. This paradigm involves deploying a model provided by a third party on local devices to perform inference…

We introduce Emergent Trust Learning (ETL), a lightweight, trust-based control algorithm that can be plugged into existing AI agents. It enables these to reach cooperation in competitive game environments under shared resources. Each agent…

As modern hardware designs grow in complexity and size, ensuring security across the confidentiality, integrity, and availability (CIA) triad becomes increasingly challenging. Information flow tracking (IFT) is a widely-used approach to…

Programmable logic controllers (PLCs) are crucial devices for implementing automated control in various industrial control systems (ICS), such as smart power grids, water treatment systems, manufacturing, and transportation systems. Owing…

We propose Data Tumbling Layer (DTL), a cryptographic scheme for non-interactive data tumbling. The core concept is to enable users to commit to specific data and subsequently re-use to the encrypted version of these data across different…

Edge intelligence enables resource-demanding Deep Neural Network (DNN) inference without transferring original data, addressing concerns about data privacy in consumer Internet of Things (IoT) devices. For privacy-sensitive applications,…

Prompt injection attacks have become an increasing vulnerability for LLM applications, where adversarial prompts exploit indirect input channels such as emails or user-generated content to circumvent alignment safeguards and induce harmful…

As machine learning (ML) technologies and applications are rapidly changing many computing domains, security issues associated with ML are also emerging. In the domain of systems security, many endeavors have been made to ensure ML model…

Manipulations of return addresses on the stack are the basis for a variety of attacks on programs written in memory unsafe languages. Dual stack schemes for protecting return addresses promise an efficient and effective defense against such…

In recent years, technology has advanced considerably with the introduction of many systems including advanced robotics, big data analytics, cloud computing, machine learning and many more. The opportunities to exploit the yet to come…