4 citations · 9 across the 12 of their papers we have counts for
5 papers · 1 filter
On the Adversarial Robustness of Instruction-Tuned Large Language Models for Code
Md Imran Hossen, Xiali Hei
The advent of instruction-tuned Large Language Models designed for coding tasks (Code LLMs) has transformed software engineering practices. However, their robustness against variou…
Leveraging Pre-trained CNNs for Efficient Feature Extraction in Rice Leaf Disease Classification
Md. Shohanur Islam Sobuj, Md. Imran Hossen, Md. Foysal Mahmud +1
Rice disease classification is a critical task in agricultural research, and in this study, we rigorously evaluate the impact of integrating feature extraction methodologies within…
Can't say cant? Measuring and Reasoning of Dark Jargons in Large Language Models
Xu Ji, Jianyi Zhang, Ziyin Zhou +5
Ensuring the resilience of Large Language Models (LLMs) against malicious exploitation is paramount, with recent focus on mitigating offensive responses. Yet, the understanding of…
Double Backdoored: Converting Code Large Language Model Backdoors to Traditional Malware via Adversarial Instruction Tuning Attacks
Md Imran Hossen, Sai Venkatesh Chilukoti, Liqun Shan +3
Instruction-tuned Large Language Models designed for coding tasks are increasingly employed as AI coding assistants. However, the cybersecurity vulnerabilities and implications ari…
Facebook Report on Privacy of fNIRS data
Md Imran Hossen, Sai Venkatesh Chilukoti, Liqun Shan +2
The primary goal of this project is to develop privacy-preserving machine learning model training techniques for fNIRS data. This project will build a local model in a centralized…