Tarun Kumar Yadav (Brigham Young University), Kent Seamons (Brigham Young University)

The FIDO2 protocol aims to strengthen or replace password authentication using public-key cryptography. FIDO2 has primarily focused on defending against attacks from afar by remote attackers that compromise a password or attempt to phish the user. In this paper, we explore threats from local attacks on FIDO2 that have received less attention---a malicious browser extension or cross-site scripting (XSS), and attackers gaining physical access to an HSK. Our systematic analysis of current implementations of FIDO2 reveals four underlying flaws, and we demonstrate the feasibility of seven attacks that exploit those flaws. The flaws include (1) Lack of confidentiality/integrity of FIDO2 messages accessible to browser extensions, (2) Broken clone detection algorithm, (3) Potential for user misunderstanding from social engineering and notification/error messages, and (4) Cookie life cycle. We build malicious browser extensions and demonstrate the attacks on ten popular web servers that use FIDO2. We also show that many browser extensions have sufficient permissions to conduct the attacks if they were compromised. A static and dynamic analysis of current browser extensions finds no evidence of the attacks in the wild. We conducted two user studies confirming that participants do not detect the attacks with current error messages, email notifications, and UX responses to the attacks. We provide an improved clone detection algorithm and recommendations for relying parties that detect or prevent some of the attacks.

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TEE-SHirT: Scalable Leakage-Free Cache Hierarchies for TEEs

Kerem Arikan (Binghamton University), Abraham Farrell (Binghamton University), Williams Zhang Cen (Binghamton University), Jack McMahon (Binghamton University), Barry Williams (Binghamton University), Yu David Liu (Binghamton University), Nael Abu-Ghazaleh (University of California, Riverside), Dmitry Ponomarev (Binghamton University)

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WIP: Auditing Artist Style Pirate in Text-to-image Generation Models

Linkang Du (Zhejiang University), Zheng Zhu (Zhejiang University), Min Chen (CISPA Helmholtz Center for Information Security), Shouling Ji (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University), Zhikun Zhang (Stanford University)

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Private Aggregate Queries to Untrusted Databases

Syed Mahbub Hafiz (University of California, Davis), Chitrabhanu Gupta (University of California, Davis), Warren Wnuck (University of California, Davis), Brijesh Vora (University of California, Davis), Chen-Nee Chuah (University of California, Davis)

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You Can Use But Cannot Recognize: Preserving Visual Privacy...

Qiushi Li (Tsinghua University), Yan Zhang (Tsinghua University), Ju Ren (Tsinghua University), Qi Li (Tsinghua University), Yaoxue Zhang (Tsinghua University)

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Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)