Ahmed Tanvir Mahdad | Searcharxiv

arXiv2026

Broken Access: On the Challenges of Screen Reader Assisted Two-Factor and Passwordless Authentication

In today's technology-driven world, web services have opened up new opportunities for blind and visually impaired people to interact independently. Securing interactions with these services is crucial; however, currently deployed authentication mainly concentrate on sighted users, overlooking the needs of the blind and visually impaired community. In this paper, we address this gap by investigating the security and accessibility aspects of these authentication when adopted by blind and visually impaired users. We model web authentication for such users as screen reader assisted authentication and introduce an evaluation framework called AWARE. Using AWARE, we then systematically assessed popular PC and smartphone-based screen readers against different authentication methods, including variants of 2FA and passwordless schemes, to simulate real-world scenarios. We analyzed these screen reader assisted authentication interactions with authentication methods in three settings: using a terminal (PC) with screen readers, a combination of the terminal (PC) and smartphone with screen readers, and smartphones with integrated screen readers. The results of our study underscore weaknesses in all of our observed screen reader assisted scenarios for real-life authentication methods. These weaknesses, encompassing specific accessibility issues caused by imprecise screen reader instructions, highlight vulnerability concerning observed scenarios for both real-world and research literature based attacks, including phishing, concurrency, fatigue, cross-service, and shoulder surfing. Broadly, our AWARE framework can be used by designers as a precursor to user studies which are typically time-consuming and tedious to perform, independently allowing to unfold security and accessibility problems early which designers can address prior to full-fledged user testing of more isolated issues.

cs.CR↗

arXiv2022

EarSpy: Spying Caller Speech and Identity through Tiny Vibrations of Smartphone Ear Speakers

Eavesdropping from the user's smartphone is a well-known threat to the user's safety and privacy. Existing studies show that loudspeaker reverberation can inject speech into motion sensor readings, leading to speech eavesdropping. While more devastating attacks on ear speakers, which produce much smaller scale vibrations, were believed impossible to eavesdrop with zero-permission motion sensors. In this work, we revisit this important line of reach. We explore recent trends in smartphone manufacturers that include extra/powerful speakers in place of small ear speakers, and demonstrate the feasibility of using motion sensors to capture such tiny speech vibrations. We investigate the impacts of these new ear speakers on built-in motion sensors and examine the potential to elicit private speech information from the minute vibrations. Our designed system EarSpy can successfully detect word regions, time, and frequency domain features and generate a spectrogram for each word region. We train and test the extracted data using classical machine learning algorithms and convolutional neural networks. We found up to 98.66% accuracy in gender detection, 92.6% detection in speaker detection, and 56.42% detection in digit detection (which is 5X more significant than the random selection (10%)). Our result unveils the potential threat of eavesdropping on phone conversations from ear speakers using motion sensors.

cs.SD↗