This study characterizes GPU resilience in Delta, a large-scale AI system that consists of 1,056 A100 and H100 GPUs, with over 1,300 petaflops of peak throughput. We used 2.5 years of operational data (11.7 million GPU hours) on GPU errors. Our major findings include: (i) H100 GPU memory resilience is worse than A100 GPU memory, with 3.2x lower per-GPU MTBE for memory errors, (ii) The GPU memory error-recovery mechanisms on H100 GPUs are insufficient to handle the increased memory capacity, (iii) H100 GPUs demonstrate significantly improved GPU hardware resilience over A100 GPUs with respect to critical hardware components, (iv) GPU errors on both A100 and H100 GPUs frequently result in job failures due to the lack of robust recovery mechanisms at the application level, and (v) We project the impact of GPU node availability on larger-scales and find that significant overprovisioning of 5% is necessary to handle GPU failures.

This study investigates the vulnerabilities of data-driven offline signature verification (DASV) systems to generative attacks and proposes robust countermeasures. Specifically, we explore the efficacy of Variational Autoencoders (VAEs) and Conditional Generative Adversarial Networks (CGANs) in creating deceptive signatures that challenge DASV systems. Using the Structural Similarity Index (SSIM) to evaluate the quality of forged signatures, we assess their impact on DASV systems built with Xception, ResNet152V2, and DenseNet201 architectures. Initial results showed False Accept Rates (FARs) ranging from 0% to 5.47% across all models and datasets. However, exposure to synthetic signatures significantly increased FARs, with rates ranging from 19.12% to 61.64%. The proposed countermeasure, i.e., retraining the models with real + synthetic datasets, was very effective, reducing FARs between 0% and 0.99%. These findings emphasize the necessity of investigating vulnerabilities in security systems like DASV and reinforce the role of generative methods in enhancing the security of data-driven systems.

This paper presents a solution that simultaneously addresses both reliability and security (RnS) in a monitoring framework. We identify the commonalities between reliability and security to guide the design of Hyper Tap, a hyper visor-level framework that efficiently supports both types of monitoring in virtualization environments. In Hyper Tap, the logging of system events and states is common across monitors and constitutes the core of the framework. The audit phase of each monitor is implemented and operated independently. In addition, Hyper Tap relies on hardware invariants to provide a strongly isolated root of trust. Hyper Tap uses active monitoring, which can be adapted to enforce a wide spectrum of RnS policies. We validate Hyper Tap by introducing three example monitors: Guest OS Hang Detection (GOSHD), Hidden Root Kit Detection (HRKD), and Privilege Escalation Detection (PED). Our experiments with fault injection and real root kits/exploits demonstrate that Hyper Tap provides robust monitoring with low performance overhead.