20 citations · 28 across the 15 of their papers we have counts for
15 papers
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…
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…
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…
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…
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…
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…
Disparate Privacy Vulnerability: Targeted Attribute Inference Attacks and Defenses
Ehsanul Kabir, Lucas Craig, Shagufta Mehnaz
As machine learning (ML) technologies become more prevalent in privacy-sensitive areas like healthcare and finance, eventually incorporating sensitive information in building data-…
Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage
Md Rafi Ur Rashid, Jing Liu, Toshiaki Koike-Akino +2
Fine-tuning large language models on private data for downstream applications poses significant privacy risks in potentially exposing sensitive information. Several popular communi…
Second-Order Information Matters: Revisiting Machine Unlearning for Large Language Models
Kang Gu, Md Rafi Ur Rashid, Najrin Sultana +1
With the rapid development of Large Language Models (LLMs), we have witnessed intense competition among the major LLM products like ChatGPT, LLaMa, and Gemini. However, various iss…
GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models
Zeyu Song, Ehsanul Kabir, Shagufta Mehnaz
Graph Neural Networks (GNNs) have become indispensable tools for learning from graph structured data, catering to various applications such as social network analysis and fraud det…
Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering
Md Rafi Ur Rashid, Vishnu Asutosh Dasu, Kang Gu +2
Federated learning (FL) has become a key component in various language modeling applications such as machine translation, next-word prediction, and medical record analysis. These a…
Attacks Meet Interpretability (AmI) Evaluation and Findings
Qian Ma, Ziping Ye, Shagufta Mehnaz
To investigate the effectiveness of the model explanation in detecting adversarial examples, we reproduce the results of two papers, Attacks Meet Interpretability: Attribute-steere…
FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks
Ehsanul Kabir, Zeyu Song, Md Rafi Ur Rashid +1
Federated learning (FL) is revolutionizing how we learn from data. With its growing popularity, it is now being used in many safety-critical domains such as autonomous vehicles and…
Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models
Shagufta Mehnaz, Sayanton V. Dibbo, Ehsanul Kabir +2
Increasing use of machine learning (ML) technologies in privacy-sensitive domains such as medical diagnoses, lifestyle predictions, and business decisions highlights the need to be…
Black-box Model Inversion Attribute Inference Attacks on Classification Models
Shagufta Mehnaz, Ninghui Li, Elisa Bertino
Increasing use of ML technologies in privacy-sensitive domains such as medical diagnoses, lifestyle predictions, and business decisions highlights the need to better understand if…