Tootfinder

LLM-Based Identification of Infostealer Infection Vectors from Screenshots: The Case of Aurora
Estelle Ruellan, Eric Clay, Nicholas Ascoli
https://arxiv.org/abs/2507.23611 https…

LLM-Based Identification of Infostealer Infection Vectors from Screenshots: The Case of Aurora
Infostealers exfiltrate credentials, session cookies, and sensitive data from infected systems. With over 29 million stealer logs reported in 2024, manual analysis and mitigation at scale are virtually unfeasible/unpractical. While most research focuses on proactive malware detection, a significant gap remains in leveraging reactive analysis of stealer logs and their associated artifacts. Specifically, infection artifacts such as screenshots, image captured at the point of compromise, are large…

Virtual Reality User Interface Design: Best Practices and Implementation
Esin Mehmedova, Santiago Berrezueta-Guzman, Stefan Wagner
https://arxiv.org/abs/2508.09358 https://

Virtual Reality User Interface Design: Best Practices and Implementation
Designing effective user interfaces (UIs) for virtual reality (VR) is essential to enhance user immersion, usability, comfort, and accessibility in virtual environments. Despite the growing adoption of VR across domains such as education, healthcare, gaming, and rehabilitation, there is a noticeable lack of unified and comprehensive design guidelines for VR UI design. To address this gap, we conducted a systematic literature review to identify existing best practices and propose complete and un…

Singing Syllabi with Virtual Avatars: Enhancing Student Engagement Through AI-Generated Music and Digital Embodiment
Xinxing Wu
https://arxiv.org/abs/2508.11872 https://

Singing Syllabi with Virtual Avatars: Enhancing Student Engagement Through AI-Generated Music and Digital Embodiment
In practical teaching, we observe that few students thoroughly read or fully comprehend the information provided in traditional, text-based course syllabi. As a result, essential details, such as course policies and learning outcomes, are frequently overlooked. To address this challenge, in this paper, we propose a novel approach leveraging AI-generated singing and virtual avatars to present syllabi in a format that is more visually appealing, engaging, and memorable. Especially, we leveraged t…

FARFETCH'D: A Side-Channel Analysis Framework for Privacy Applications on Confidential Virtual Machines
Ruiyi Zhang, Albert Cheu, Adria Gascon, Daniel Moghimi, Phillipp Schoppmann, Michael Schwarz, Octavian Suciu
https://arxiv.org/abs/2506.15924

FARFETCH'D: A Side-Channel Analysis Framework for Privacy Applications on Confidential Virtual Machines
Confidential virtual machines (CVMs) based on trusted execution environments (TEEs) enable new privacy-preserving solutions. Yet, they leave side-channel leakage outside their threat model, shifting the responsibility of mitigating such attacks to developers. However, mitigations are either not generic or too slow for practical use, and developers currently lack a systematic, efficient way to measure and compare leakage across real-world deployments. In this paper, we present FARFETCH'D, an ope…