A Large-Scale Survey of Password Entry Practices on Non-Desktop Devices

• URL: http://arxiv.org/abs/2409.03044v1
• Date: Wed, 4 Sep 2024 19:28:36 GMT
• Title: A Large-Scale Survey of Password Entry Practices on Non-Desktop Devices
• Authors: John Sadik, Scott Ruoti,
• Abstract summary: We find that password entry on devices without password managers is a common occurrence and comes with significant usability challenges. These challenges lead users to weaken their passwords to increase the ease of entry. We conclude this paper with a discussion of how future research could address these challenges and encourage users to adopt generated passwords.
• Score: 2.8698289487200856
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Password managers encourage users to generate passwords to improve their security. However, research has shown that users avoid generating passwords, often giving the rationale that it is difficult to enter generated passwords on devices without a password manager. In this paper, we conduct a survey ($n=999$) of individuals from the US, UK, and Europe, exploring the range of devices on which they enter passwords and the challenges associated with password entry on those devices. We find that password entry on devices without password managers is a common occurrence and comes with significant usability challenges. These usability challenges lead users to weaken their passwords to increase the ease of entry. We conclude this paper with a discussion of how future research could address these challenges and encourage users to adopt generated passwords.

Related papers

• Nudging Users to Change Breached Passwords Using the Protection Motivation Theory [58.87688846800743]
  We draw on the Protection Motivation Theory (PMT) to design nudges that encourage users to change breached passwords. Our study contributes to PMT's application in security research and provides concrete design implications for improving compromised credential notifications.
  arXiv  Detail & Related papers  (2024-05-24T07:51:15Z)
• Protecting Copyrighted Material with Unique Identifiers in Large Language Model Training [55.321010757641524]
  A major public concern regarding the training of large language models (LLMs) is whether they abusing copyrighted online text. Previous membership inference methods may be misled by similar examples in vast amounts of training data. We propose an alternative textitinsert-and-detection methodology, advocating that web users and content platforms employ textbftextitunique identifiers.
  arXiv  Detail & Related papers  (2024-03-23T06:36:32Z)
• Passwords Are Meant to Be Secret: A Practical Secure Password Entry Channel for Web Browsers [7.049738935364298]
  Malicious client-side scripts and browser extensions can steal passwords after they have been autofilled by the manager into the web page. This paper explores what role the password manager can take in preventing the theft of autofilled credentials without requiring a change to user behavior.
  arXiv  Detail & Related papers  (2024-02-09T03:21:14Z)
• PassViz: A Visualisation System for Analysing Leaked Passwords [2.2530496464901106]
  PassViz is a command-line tool for visualising and analysing leaked passwords in a 2-D space. We show how PassViz can be used to visually analyse different aspects of leaked passwords and to facilitate the discovery of previously unknown password patterns.
  arXiv  Detail & Related papers  (2023-09-22T16:06:26Z)
• PassGPT: Password Modeling and (Guided) Generation with Large Language Models [59.11160990637616]
  We present PassGPT, a large language model trained on password leaks for password generation. We also introduce the concept of guided password generation, where we leverage PassGPT sampling procedure to generate passwords matching arbitrary constraints.
  arXiv  Detail & Related papers  (2023-06-02T13:49:53Z)
• RiDDLE: Reversible and Diversified De-identification with Latent Encryptor [57.66174700276893]
  This work presents RiDDLE, short for Reversible and Diversified De-identification with Latent Encryptor. Built upon a pre-learned StyleGAN2 generator, RiDDLE manages to encrypt and decrypt the facial identity within the latent space.
  arXiv  Detail & Related papers  (2023-03-09T11:03:52Z)
• Targeted Honeyword Generation with Language Models [5.165256397719443]
  Honeywords are fictitious passwords inserted into databases to identify password breaches. Major difficulty is how to produce honeywords that are difficult to distinguish from real passwords.
  arXiv  Detail & Related papers  (2022-08-15T00:06:29Z)
• Skeptic: Automatic, Justified and Privacy-Preserving Password Composition Policy Selection [44.040106718326605]
  The choice of password composition policy to enforce on a password-protected system represents a critical security decision. In practice, this choice is not usually rigorous or justifiable, with a tendency for system administrators to choose password composition policies based on intuition alone. We propose a novel methodology that draws on password probability distributions constructed from large sets of real-world password data.
  arXiv  Detail & Related papers  (2020-07-07T22:12:13Z)
• DeepMnemonic: Password Mnemonic Generation via Deep Attentive Encoder-Decoder Model [26.797370435988853]
  We bridge the gap between strong password generation and the usability of strong passwords. We propose to automatically generate textual password mnemonics, i.e., natural language sentences, which are intended to help users better memorize passwords.
  arXiv  Detail & Related papers  (2020-06-24T04:05:48Z)
• Lost in Disclosure: On The Inference of Password Composition Policies [43.17794589897313]
  We study how password composition policies influence the distribution of user-chosen passwords on a system. We suggest a simple approach that produces more reliable results. We present pol-infer, a tool that implements this approach, and demonstrates its use inferring password composition policies.
  arXiv  Detail & Related papers  (2020-03-12T15:27:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.