206 citations · 296 across the 14 of their papers we have counts for
14 papers
MindReader: Using LLMs to Encourage Memorable and Secure Password Replacement
Anna Gerchanovsky, Lujo Bauer, Michael K. Reiter
We report on the design and evaluation of MindReader, a tool that helps a user replace her password when she is required to do so. Left to their own devices, users tend to replace…
Security and Privacy Prompts in the Wild: What Users Ask LLMs and How LLMs Respond
Hobin Kim, Xiaoyuan Wu, Omer Akgul +2
Large language models (LLMs) are widely used to fulfill users' information needs; users ask LLMs about the weather, pose educational questions, and consult them for legal assistanc…
The Impact of Device Type, Data Practices, and Use Case Scenarios on Privacy Concerns about Eye-tracked Augmented Reality in the United States and Germany
Efe Bozkir, Babette Bühler, Xiaoyuan Wu +3
Augmented reality technology will likely be prevalent with more affordable head-mounted displays. Integrating novel interaction modalities, such as eye trackers into head-mounted d…
The Impact of Exposed Passwords on Honeyword Efficacy
Zonghao Huang, Lujo Bauer, Michael K. Reiter
Honeywords are decoy passwords that can be added to a credential database; if a login attempt uses a honeyword, this indicates that the site's credential database has been leaked.…
Group-based Robustness: A General Framework for Customized Robustness in the Real World
Weiran Lin, Keane Lucas, Neo Eyal +3
Machine-learning models are known to be vulnerable to evasion attacks that perturb model inputs to induce misclassifications. In this work, we identify real-world scenarios where t…
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators
Keane Lucas, Matthew Jagielski, Florian Tramèr +2
It is becoming increasingly imperative to design robust ML defenses. However, recent work has found that many defenses that initially resist state-of-the-art attacks can be broken…