Abstract Large Language Models (LLMs) are progressively being utilized as machine
learning services and interface tools for various applications. However, the security …

Memory isolation is critical for system reliability, security, and safety. Unfortunately, read
disturbance can break memory isolation in modern DRAM chips. For example, RowHammer …

As the machine learning-related technology has made great progress in recent years, deep
neural networks are widely used in many scenarios, including security-critical ones, which …

Abstract Vision Transformers (ViTs), which made a splash in the field of computer vision
(CV), have shaken the dominance of convolutional neural networks (CNNs). However, in the …

Given the power of vision transformers a new learning paradigm pre-training and then
prompting makes it more efficient and effective to address downstream visual recognition …

We provide an overview of recent developments and future directions in the RowHammer
vulnerability that plagues modern DRAM (Dynamic Random Memory Access) chips, which …

DRAM chips are increasingly more vulnerable to read-disturbance phenomena (eg,
RowHammer and RowPress), where repeatedly accessing DRAM rows causes bitflips in …

We introduce ABACuS, a new low-cost hardware-counterbased RowHammer mitigation
technique that performance-, energy-, and area-efficiently scales with worsening …

To understand and improve DRAM performance, reliability, security, and energy efficiency,
prior works study characteristics of commodity DRAM chips. Unfortunately, state-of-the-art …

Bit-Flip Attacks (BFAs) involve adversaries manipulating a model's parameter bits to
undermine its accuracy significantly. They typically target the most vulnerable parameters …