The market size of augmented reality and virtual reality (AR/VR) has been expanding rapidly in recent years, with the use of face-mounted headsets extending beyond gaming to various application sectors, such as education, healthcare, and the military. Despite the rapid growth, the understanding of information leakage through sensor-rich headsets remains in its infancy. Some of the headset's built-in sensors do not require users' permission to access, and any apps and websites can acquire their readings. While theseunrestricted sensors are generally considered free of privacy risks, we find that an adversary could uncover private information by scrutinizing sensor readings, making existing AR/VR apps and websites potential eavesdroppers. In this work, we investigate a novel, unobtrusive privacy attack called FaceReader, which reconstructs high-quality vital sign signals (breathing and heartbeat patterns) based on unrestricted AR/VR motion sensors. FaceReader is built on the key insight that the headset is closely mounted on the user's face, allowing the motion sensors to detect subtle facial vibrations produced by users' breathing and heartbeats. Based on the reconstructed vital signs, we further investigate three more advanced attacks, including gender recognition, user re-identification, and body fat ratio estimation. Such attacks pose severe privacy concerns, as an adversary may obtain users' sensitive demographic/physiological traits and potentially uncover their real-world identities. Compared to prior privacy attacks relying on speeches and activities, FaceReader targets spontaneous breathing and heartbeat activities that are naturally produced by the human body and are unobtrusive to victims. In particular, we design an adaptive filter to dynamically mitigate the impacts of body motions. We further employ advanced deep-learning techniques to reconstruct vital sign signals, achieving signal qualities comparable to those of dedicated medical instruments, as well as deriving sensitive gender, identity, and body fat information. We conduct extensive experiments involving 35 users on three types of mainstream AR/VR headsets across 3 months. The results reveal that FaceReader can reconstruct vital signs with low mean errors and accurately detect gender (over 93.33%). The attack can also link/re-identify users across different apps, websites, and longitudinal sessions with over 97.83% accuracy. Furthermore, we present the first successful attempt at revealing body fat information from motion sensor data, achieving a remarkably low estimation error of 4.43%.

In AR/VR devices, the voice interface, serving as one of the primary AR/VR control mechanisms, enables users to interact naturally using speeches (voice commands) for accessing data, controlling applications, and engaging in remote communication/meetings. Voice authentication can be adopted to protect against unauthorized speech inputs. However, existing voice authentication mechanisms are usually susceptible to voice spoofing attacks and are unreliable under the variations of phonetic content. In this work, we propose SAFARI, a spoofing-resistant and text-independent speech authentication system that can be seamlessly integrated into AR/VR voice interfaces. The key idea is to elicit phonetic-invariant biometrics from the facial muscle vibrations upon the headset. During speech production, a user's facial muscles are deformed for articulating phoneme sounds. The facial deformations associated with the phonemes are referred to as visemes. They carry rich biometrics of the wearer's muscles, tissue, and bones, which can propagate through the head and vibrate the headset. SAFARI aims to derive reliable facial biometrics from the viseme-associated facial vibrations captured by the AR/VR motion sensors. Particularly, it identifies the vibration data segments that contain rich viseme patterns (prominent visemes) less susceptible to phonetic variations. Based on the prominent visemes, SAFARI learns on the correlations among facial vibrations of different frequencies to extract biometric representations invariant to the phonetic context. The key advantages of SAFARI are that it is suitable for commodity AR/VR headsets (no additional sensors) and is resistant to voice spoofing attacks as the conductive property of the facial vibrations prevents biometric disclosure via the air media or the audio channel. To mitigate the impacts of body motions in AR/VR scenarios, we also design a generative diffusion model trained to reconstruct the viseme patterns from the data distorted by motion artifacts. We conduct extensive experiments with two representative AR/VR headsets and 35 users under various usage and attack settings. We demonstrate that SAFARI can achieve over 96% true positive rate on verifying legitimate users while successfully rejecting different kinds of spoofing attacks with over 97% true negative rates.

Two-factor authentication (2FA) systems aim to secure user accounts, provided that either the password or the second factor device remains uncompromised. However, in this research, we challenge this perception and analyze the security of FIDO2 hardware security keys, which are increasingly used in 2FA and passwordless systems. Specifically, we develop an attack framework, analyze the underlying protocols of FIDO2, and examine the associated OS-level security. Through practical demonstrations, we illustrate how adversaries can exploit this framework and OS-level security measures to execute our designed attack, known as FIDOLA (FIDO2 Deception Attack via Overlays exploiting Limited Display Authenticators). Our attack framework injects hidden login sessions, either into the same service the user intends to authenticate with or into a different service. It deceives users into approving the attackers request using the limited display of authenticators. This cross-service attack raises concerns about compromising more sensitive accounts (e.g., financial) when users log into less sensitive ones. Our attack poses a practical and fundamental threat not addressed in the FIDO specification or prior research. Unlike prior research, our demonstration exposes FIDO2 authenticator vulnerabilities in real-world 2FA and passwordless setups, where OS-level security mitigates traditional concurrent attacks (simultaneous authentication attempts by the attacker). To assess our attacks effectiveness, we conducted a user study, revealing that users approved approximately 95.55% of cross-service attacks when presented with a screen overlay.

Notification-based authentication is an emerging Two-Factor Authentication (2FA) and passwordless solution that leverages interactive notifications on mobile devices to establish an additional layer of security beyond passwords. This method has gained popularity due to its convenience and ease of deployment in organizational settings. In this work, we aim to evaluate the effectiveness of notification-based authentication systems when a malicious entity is present on the user's computer, such as a keylogger or malicious extension, without compromising the mobile devices or communication channels. Furthermore, we investigate how the lack of information provided to users during the authentication workflow can lead to the approval of malicious authentication requests. Notably, we highlight the vulnerability of cross-service attacks, where an attacker authenticates to Service B while the user is attempting to authenticate to Service A. Our proof-of-concept attack program demonstrates the susceptibility of various notification-based authentication systems, and our user study reveals an alarming 82.2% cross-service attack success rate. These findings suggest a potential vulnerability in notification-based authentication systems, where the attacker compromise user account without compromising possession-factor device, such as smartphones.

Reducing the level of user effort involved in traditional two-factor authentication (TFA) constitutes an important research topic. An interesting representative approach, Sound-Proof , leverages ambient sounds to detect the proximity between the second-factor device (phone) and the login terminal (browser), and it eliminates the need for the user to transfer PIN codes. In this article, we identify a weakness of the Sound-Proof system that makes it completely vulnerable to passive “environment guessing” and active “environment manipulating” remote attackers and proximity attackers. Addressing these security issues, we propose Listening-Watch , a new TFA mechanism based on a wearable device (watch/bracelet) and active browser-generated random speech sounds. As the user attempts to log in, the browser populates a short random code encoded into speech, and the login succeeds if the watch’s audio recording contains this code (decoded using speech recognition ) and is similar enough to the browser’s audio recording. The remote attacker, who has guessed/manipulated the user’s environment, will be defeated, since authentication success relies upon the presence of the random code in watch’s recordings. The proximity attacker will also be defeated unless it is extremely close (<50 cm) to the watch, since the wearable microphones are usually designed to capture only nearby sounds (e.g., voice commands).

Malware-infected terminals pose a pervasive threat to authentication systems. As password-only authentication cannot adequately protect against malware on terminals, the literature proposes several authentication methods claiming to provide security in the presence of significant security threats, including infected terminals. Most methods incorporate a password-independent factor in the authentication process to mitigate these threats. According to the community view in the literature, 2FA-oriented methods appear to be secure in the presence of malware on the authentication terminal. In this work, we systematize these 2FA-based academic schemes' threat models and authentication procedures to examine how they ensure security at every step of the authentication process. Additionally, we present an active concurrent attack framework named CSI(Concurrent Session Injection) and have done a comprehensive analysis of studied academic authentication systems against it. Furthermore, we systematize secure authentication systems from the literature that claim to provide protection against user terminal malware and concurrent attacks and point out their potential vulnerabilities. Our research emphasizes the significance of taking proper security measures against such threats and creates the opportunity to design more secure authentication systems in future research.