26 citations · 56 across the 8 of their papers we have counts for
11 papers
COPEX: Benchmarking LLM Robustness to Adversarial Context Across Model Context Protocol (MCP) Layers
Nahom Birhan, Mehrdad Rostamzadeh, Sidhant Narula +3
Large language models increasingly mediate tool use in Model Context Protocol (MCP) systems, where adversarial influence may enter through user instructions, tool schemas, tool out…
FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design
Mahmoud Nazzal, Khoa Nguyen, Deepak Vungarala +4
AI hardware design is advancing rapidly, driven by the promise of design automation to make chip development faster, more efficient, and more accessible to a wide range of users. A…
ViGText: Deepfake Image Detection with Vision-Language Model Explanations and Graph Neural Networks
Ahmad ALBarqawi, Mahmoud Nazzal, Issa Khalil +2
The rapid rise of deepfake technology, which produces realistic but fraudulent digital content, threatens the authenticity of media. Traditional deepfake detection approaches often…
Demo: SGCode: A Flexible Prompt-Optimizing System for Secure Generation of Code
Khiem Ton, Nhi Nguyen, Mahmoud Nazzal +6
This paper introduces SGCode, a flexible prompt-optimizing system to generate secure code with large language models (LLMs). SGCode integrates recent prompt-optimization approaches…
PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs)
Mahmoud Nazzal, Issa Khalil, Abdallah Khreishah +1
The capability of generating high-quality source code using large language models (LLMs) reduces software development time and costs. However, they often introduce security vulnera…
SA-DS: A Dataset for Large Language Model-Driven AI Accelerator Design Generation
Deepak Vungarala, Mahmoud Nazzal, Mehrdad Morsali +4
In the ever-evolving landscape of Deep Neural Networks (DNN) hardware acceleration, unlocking the true potential of systolic array accelerators has long been hindered by the daunti…
Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection
Mahmoud Nazzal, Issa Khalil, Abdallah Khreishah +2
Malicious domain detection (MDD) is an open security challenge that aims to detect if an Internet domain is associated with cyber-attacks. Among many approaches to this problem, gr…
Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach
Mahmoud Nazzal, Abdallah Khreishah, Joyoung Lee +3
Prediction of taxi service demand and supply is essential for improving customer's experience and provider's profit. Recently, graph neural networks (GNNs) have been shown promisin…
Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions
Izzat Alsmadi, Kashif Ahmad, Mahmoud Nazzal +4
The growing use of social media has led to the development of several Machine Learning (ML) and Natural Language Processing(NLP) tools to process the unprecedented amount of social…
Deep Learning-Based Optimal RIS Interaction Exploiting Previously Sampled Channel Correlations
Mehmet Ali Aygul, Mahmoud Nazzal, Huseyin Arslan
The reconfigurable intelligent surface (RIS) technology has attracted interest due to its promising coverage and spectral efficiency features. However, some challenges need to be a…
Primary User Emulation and Jamming Attack Detection in Cognitive Radio via Sparse Coding
H. M. Furqan, M. A. Aygul, M. Nazzal +1
Cognitive radio is an intelligent and adaptive radio that improves the utilization of the spectrum by its opportunistic sharing. However, it is inherently vulnerable to primary use…