Sarah Tabassum (University of North Carolina at Charlotte, USA), Narges Zare (University of North Carolina at Charlotte, USA), Cori Faklaris(University of North Carolina at Charlotte, USA)

In today’s digital world, migrants stay connected to family, institutions, and services across borders, but this reliance on digital communication also exposes them to unfamiliar risks when they enter new technological and cultural environments. Educational migrants (also known as international students) depend on online platforms to manage admission, housing, work, and everyday life in the United States. Yet this transition often introduces an unfamiliar and fragmented digital ecosystem where they encounter privacy and security threats such as phishing, identity fraud, and cross-channel scams. Existing security tools rarely consider the situated vulnerabilities of newcomers who must interpret these threats without local knowledge or culturally familiar cues. To investigate these challenges, we conducted participatory design sessions with 22 educational migrants from Global South countries studying in the United States. Using inductive open coding within a reflexive thematic analysis framework, we identified seven themes of desired features. Participants proposed a range of support mechanisms, including transparent reporting and verification workflows, scam filtering, migrant-focused scam databases, and university-integrated safety tools Participants also mapped their concepts to high-level AI capabilities, emphasizing detection, identification, and interpretable explanations. Our findings highlight the need for transparent, culturally grounded, and context-aware digital safety supports for newcomers during their early experiences in the U.S. digital ecosystem.

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Work-in-progress: JaVulIn: Scalable Vulnerability Injection for JavaScript Web Applications

Dominic Troppmann (CISPA Helmholtz Center for Information Security), Cristian-Alexandru Staicu (Endor Labs), Aurore Fass (Inria Centre at Université Côte d’Azur)

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ReFuzz: Reusing Tests for Processor Fuzzing with Contextual Bandits

Chen Chen (Texas A&M University, USA), Zaiyan Xu (Texas A&M University, USA), Mohamadreza Rostami (Technische Universitat Darmstadt, Germany), David Liu (Texas A&M University, USA), Dileep Kalathil (Texas A&M University, USA), Ahmad-Reza Sadeghi (Technische Universitat Darmstadt, Germany), Jeyavijayan (JV) Rajendran (Texas A&M University, USA)

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