activity
20202026
most citedAre Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models

20 citations · 28 across the 12 of their papers we have counts for

collaborators

15 papers

cs.CR2026

LogicEval: A Systematic Framework for Evaluating Automated Repair Techniques for Logical Vulnerabilities in Real-World Software

Syed Md Mukit Rashid, Abdullah Al Ishtiaq, Kai Tu +7

Logical vulnerabilities in software stem from flaws in program logic rather than memory safety, which can lead to critical security failures. Although existing automated program re…

cs.CV2026

Robustness of Vision Language Models Against Split-Image Harmful Input Attacks

Md Rafi Ur Rashid, MD Sadik Hossain Shanto, Vishnu Asutosh Dasu +1

Vision-Language Models (VLMs) are now a core part of modern AI. Recent work proposed several visual jailbreak attacks using single/ holistic images. However, contemporary VLMs demo…

cs.CR2026

Baiting AI: Deceptive Adversary Against AI-Protected Industrial Infrastructures

Aryan Pasikhani, Prosanta Gope, Yang Yang +2

This paper explores a new cyber-attack vector targeting Industrial Control Systems (ICS), particularly focusing on water treatment facilities. Developing a new multi-agent Deep Rei…

cs.LG2025

From Insight to Exploit: Leveraging LLM Collaboration for Adaptive Adversarial Text Generation

Najrin Sultana, Md Rafi Ur Rashid, Kang Gu +1

LLMs can provide substantial zero-shot performance on diverse tasks using a simple task prompt, eliminating the need for training or fine-tuning. However, when applying these model…

cs.LG2025

Benchmarking Robust Aggregation in Decentralized Gradient Marketplaces

Zeyu Song, Sainyam Galhotra, Shagufta Mehnaz

The rise of distributed and privacy-preserving machine learning has sparked interest in decentralized gradient marketplaces, where participants trade intermediate artifacts like gr…

cs.CL2025

Chain-of-Thought Driven Adversarial Scenario Extrapolation for Robust Language Models

Md Rafi Ur Rashid, Vishnu Asutosh Dasu, Ye Wang +2

Large Language Models (LLMs) exhibit impressive capabilities, but remain susceptible to a growing spectrum of safety risks, including jailbreaks, toxic content, hallucinations, and…