46 citations · 133 across the 34 of their papers we have counts for
7 papers · 1 filter
EVAX: Towards a Practical, Pro-active & Adaptive Architecture for High Performance & Security
Samira Mirbagher Ajorpaz, Daniel Moghimi, Jeffrey Neal Collins +3
This paper provides an end-to-end solution to defend against known microarchitectural attacks such as speculative execution attacks, fault-injection attacks, covert and side channe…
PAGE404: Breaking GPU Isolation Through Hardware-assisted Page Migration
Ravan Nazaraliyev, Yicheng Zhang, Nael Abu-Ghazaleh
NVIDIA's Grace Hopper (GH200) superchip represents a new generation of heterogeneous computing, tightly integrating a 72-core Arm-based CPU with a Hopper GPU on a single module via…
Visual Grounding Safety in Vision-Language Models
Erfan Shayegani, Kundan Krishna, Yue Dong +3
Vision-language models (VLMs) are increasingly trained to generate structured outputs like points and bounding boxes that downstream interfaces, agents, and robots can act on, yet…
Microflow: Microarchitectural Causal Observability for Deep Cross-Layer Analysis and Optimization
Saber Ganjisaffar, Chengyu Song, Nael Abu-Ghazaleh
Modern computer architecture relies heavily on simulation to identify bottlenecks and evaluate optimizations. However, existing microarchitectural performance analysis methods are…
Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs
Md Abdullah Al Mamun, Ngoc Phu Doan, Pedram Zaree +2
Large Language Models are increasingly trained on proprietary or sensitive data, from private healthcare and financial records to user conversations containing secrets. Ensuring th…
VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors
Haz Sameen Shahgir, Xiaofu Chen, Yu Fu +4
Vision-language models (VLMs) have achieved impressive performance across a wide range of multimodal tasks. However, they often fail on tasks that require fine-grained visual perce…
AttenMIA: LLM Membership Inference Attack through Attention Signals
Pedram Zaree, Md Abdullah Al Mamun, Yue Dong +2
Large Language Models (LLMs) are increasingly deployed to enable or improve a multitude of real-world applications. Given the large size of their training data sets, their tendency…