Since the first Graphical User Interface (GUI) prototype was invented in the 1970s, GUI systems have been deployed into various personal computer systems and server platforms. Recently, with the development of artificial intelligence (AI) technology, malicious malware powered by AI is emerging as a potential threat to GUI systems. This type of AI-based cybersecurity attack, targeting at GUI systems, is explored in this paper. It is twofold: (1) A malware is designed to attack the existing GUI system by using AI-based object recognition techniques. (2) Its defensive methods are discovered by generating adversarial examples and other methods to alleviate the threats from the intelligent GUI attack. The results have shown that a generic GUI attack can be implemented and performed in a simple way based on current AI techniques and its countermeasures are temporary but effective to mitigate the threats of GUI attack so far.
AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learning
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence（信息产业部模式分析与机器智能重点实验室）
Toward a Human-Centered Evaluation Framework for Trustworthy LLM-Powered GUI Agents
University of Notre Dame（圣约翰大学）；Northeastern University（东北大学）；Virginia Tech（弗吉尼亚理工大学）；University of Washington（华盛顿大学）；Johns Hopkins University（约翰霍普金斯大学）
Defensive Adversarial CAPTCHA: A Semantics-Driven Framework for Natural Adversarial Example Generation
School of Computer and Information Engineering, Xiamen University of Technology（厦门理工学院计算机与信息工程学院）；School of Computer Science, Engineering Research Center of Machine Learning and Industry Intelligence, Sichuan University（四川大学计算机科学学院、机器学习与工业智能工程研究中心）；Department of Electrical and Electronic Engineering, University of Hong Kong（香港大学电子与电气工程系）；Department of Computer and Information Science, Faculty of Science and Technology, University of Macau（澳门大学科学与技术学院计算机与信息科学系）；Institute of Space Internet, Fudan University（复旦大学空间互联网研究所）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61）；School of Computer Engineering, Nanyang Technological University（南洋理工大学计算机工程学院）
Rain often poses inevitable threats to deep neural network (DNN) based perception systems, and a comprehensive investigation of the potential risks of the rain to DNNs is of great importance. However, it is rather difficult to collect or synthesize rainy images that can represent all rain situations that would possibly occur in the real world. To this end, in this paper, we start from a new perspective and propose to combine two totally different studies, i.e., rainy image synthesis and adversarial attack. We first present an adversarial rain attack, with which we could simulate various rain s…
The Obvious Invisible Threat: LLM-Powered GUI Agents' Vulnerability to Fine-Print Injections
University of Notre Dame（圣母大学）；Northeastern University（东北大学）；University of Washington（华盛顿大学）；Virginia Tech（弗吉尼亚理工大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Training pipelines for machine learning (ML) based malware classification often rely on crowdsourced threat feeds, exposing a natural attack injection point. In this paper, we study the susceptibility of feature-based ML malware classifiers to backdoor poisoning attacks, specifically focusing on challenging "clean label" attacks where attackers do not control the sample labeling process. We propose the use of techniques from explainable machine learning to guide the selection of relevant features and values to create effective backdoor triggers in a model-agnostic fashion. Using multiple refer…
