2 citations · 2 across the 3 of their papers we have counts for
3 papers
Faults That Fortify: CNN Adversarial Robustness via GPU Undervolting
Behnam Omidi, Ahmad Tahmasivand, Husam Alsyouri +4
Convolutional Neural Networks (CNNs) face a dual challenge: vulnerability to adversarial attacks and prohibitive training cost. Adversarial training is effective but expensive, a b…
LM-Fix: Lightweight Bit-Flip Detection and Rapid Recovery Framework for Language Models
Ahmad Tahmasivand, Noureldin Zahran, Saba Al-Sayouri +2
This paper presents LM-Fix, a lightweight detection and rapid recovery framework for faults in large language models (LLMs). Existing integrity approaches are often heavy or slow f…
On Jailbreaking Quantized Language Models Through Fault Injection Attacks
Noureldin Zahran, Ahmad Tahmasivand, Ihsen Alouani +2
The safety alignment of Language Models (LMs) is a critical concern, yet their integrity can be challenged by direct parameter manipulation attacks, such as those potentially induc…