Related papers: Narrowing the Gap between TEEs Threat Model and De…

Architectures relying on continuous authentication require a secure way to challenge the user's identity without trusting that the Continuous Authentication Subsystem (CAS) has not been compromised, i.e., that the response to the layer…

The Android ecosystem relies on either TrustZone (e.g., OP-TEE, QTEE, Trusty) or trusted hypervisors (pKVM, Gunyah) to isolate security-sensitive services from malicious apps and Android bugs. TrustZone allows any secure world code to…

In order to determine the user's trust is a growing concern for ensuring privacy and security in a cloud computing environment. In cloud, user's data is stored in one or more remote server(s) which poses more security challenges for the…

This report evaluates the performance impact of enabling Trusted Execution Environments (TEE) on NVIDIA Hopper GPUs for large language model (LLM) inference tasks. We benchmark the overhead introduced by TEE mode across various LLMs and…

Trusted Execution Environments (TEEs) are hardware-enforced memory isolation units, emerging as a pivotal security solution for security-critical applications. TEEs, like Intel SGX and ARM TrustZone, allow the isolation of confidential code…

Intel Trust Domain Extensions (TDX) is a new architectural extension in the 4th Generation Intel Xeon Scalable Processor that supports confidential computing. TDX allows the deployment of virtual machines in the Secure-Arbitration Mode…

With the advancement in technology, Cloud computing always amazes the world with revolutionizing solutions that automate and simplify complex computational tasks. The advantages like no maintenance cost, accessibility, data backup,…

Edge computing promises to reshape the centralized nature of today's cloud-based applications by bringing computing resources, at least in part, closer to the user. Reasons include the increasing need for real-time (short-delay,…

Hardware-assisted trusted execution environments (TEEs) are critical building blocks of many modern applications. However, they have a one-way isolation model that introduces a semantic gap between a TEE and its outside world. This lack of…

Recent advances in Transformer models, e.g., large language models (LLMs), have brought tremendous breakthroughs in various artificial intelligence (AI) tasks, leading to their wide applications in many security-critical domains. Due to…

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…

We develop the concept of Trusted and Confidential Program Analysis (TCPA) which enables program certification to be used where previously there was insufficient trust. Imagine a scenario where a producer may not be trusted to certify its…

In recent years, the increasing awareness of cybersecurity has led to a heightened focus on information security within hardware devices and products. Incorporating Trusted Execution Environments (TEEs) into product designs has become a…

A collaboration between dataset owners and model owners is needed to facilitate effective machine learning (ML) training. During this collaboration, however, dataset owners and model owners want to protect the confidentiality of their…

We study secure and privacy-preserving data analysis based on queries executed on samples from a dataset. Trusted execution environments (TEEs) can be used to protect the content of the data during query computation, while supporting…

Deep neural network (DNN) models have become prevalent in edge devices for real-time inference. However, they are vulnerable to model extraction attacks and require protection. Existing defense approaches either fail to fully safeguard…

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…

The rise of autonomous AI agents in enterprise and industrial environments introduces a critical challenge: how to securely assign, verify, and manage their identities across distributed systems. Existing identity frameworks based on API…

Computer systems often provide hardware support for isolation mechanisms like privilege levels, virtual memory, or enclaved execution. Over the past years, several successful software-based side-channel attacks have been developed that…

Allowing a compromised device to receive privacy-sensitive sensor readings, or to operate a safety-critical actuator, carries significant risk. Usually, such risks are mitigated by validating the device's security state with remote…