Related papers: Characterizing Trust Boundary Vulnerabilities in T…

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…

High-confidence computing relies on trusted instructional set architecture, sealed kernels, and secure operating systems. Cloud computing depends on trusted systems for virtualization tasks. Branch predictions and pipelines are essential in…

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…

Kubernetes has emerged as the de facto standard for container orchestration. Unfortunately, its increasing popularity has also made it an attractive target for malicious actors. Despite extensive research on securing Kubernetes, little…

In recent years, we have witnessed unprecedented growth in using hardware-assisted Trusted Execution Environments (TEE) or enclaves to protect sensitive code and data on commodity devices thanks to new hardware security features, such as…

Personal cryptographic keys are the foundation of many secure services, but storing these keys securely is a challenge, especially if they are used from multiple devices. Storing keys in a centralized location, like an Internet-accessible…

Confidential computing protects data in use within Trusted Execution Environments (TEEs), but current TEEs provide little support for secure communication between components. As a result, pipelines of independently developed and deployed…

Securing sensitive operations in today's interconnected software landscape is crucial yet challenging. Modern platforms rely on Trusted Execution Environments (TEEs), such as Intel SGX and ARM TrustZone, to isolate security sensitive code…

Mobile devices rely on Trusted Execution Environments (TEEs) to execute security-critical code and protect sensitive assets. This security-critical code is modularized in components known as Trusted Applications (TAs). Vulnerabilities in…

With the increasing popularity of Internet of Things (IoT) devices, securing sensitive user data has emerged as a major challenge. These devices often collect confidential information, such as audio and visual data, through peripheral…

Confidential Virtual Machines (CVMs) protect data in use by running workloads within hardware-enforced Trusted Execution Environments (TEEs). However, existing CVM attestation mechanisms only certify what code is running, not where it is…

A trusted execution environment (TEE) such as Intel Software Guard Extension (SGX) runs a remote attestation to prove to a data owner the integrity of the initial state of an enclave, including the program to operate on her data. For this…

This paper introduces BeaCon, a novel tool for the automated generation of adjustable container security policies. Unlike prior approaches, BeaCon leverages dynamic analysis to simulate realistic environments, uncovering container execution…

The big data industry is facing new challenges as concerns about privacy leakage soar. One of the remedies to privacy breach incidents is to encapsulate computations over sensitive data within hardware-assisted Trusted Execution…

Over the past few years, several research groups have introduced innovative hardware designs for Trusted Execution Environments (TEEs), aiming to secure applications against potentially compromised privileged software, including the kernel.…

During the past few years, we have witnessed various efforts to provide confidentiality and integrity for applications running in untrusted environments such as public clouds. In most of these approaches, hardware extensions such as Intel…

Trusted execution environment (TEE) technology has found many applications in mitigating various security risks in an efficient manner, which is attractive for critical infrastructure protection. First, the natural of critical…

The deployment of large language models (LLMs) on third-party devices requires new ways to protect model intellectual property. While Trusted Execution Environments (TEEs) offer a promising solution, their performance limits can lead to a…

An increasing number of distributed platforms combine Trusted Execution Environments (TEEs) with blockchains. Indeed, many hail the combination of TEEs and blockchains a good "marriage": TEEs bring confidential computing to the blockchain…

Transfer learning from pre-trained encoders has become essential in modern machine learning, enabling efficient model adaptation across diverse tasks. However, this combination of pre-training and downstream adaptation creates an expanded…