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Passwords Are Meant to Be Secret: A Practical Secure Password Entry Channel for Web Browsers

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arxiv 2402.06159 v1 pith:VGMQOIY3 submitted 2024-02-09 cs.CR

classification cs.CR
keywords passwordbrowserdesignmanagerbehaviorextensionsmaliciouspasswords
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Password-based authentication faces various security and usability issues. Password managers help alleviate some of these issues by enabling users to manage their passwords effectively. However, malicious client-side scripts and browser extensions can steal passwords after they have been autofilled by the manager into the web page. In this paper, we explore what role the password manager can take in preventing the theft of autofilled credentials without requiring a change to user behavior. To this end, we identify a threat model for password exfiltration and then use this threat model to explore the design space for secure password entry implemented using a password manager. We identify five potential designs that address this issue, each with varying security and deployability tradeoffs. Our analysis shows the design that best balances security and usability is for the manager to autofill a fake password and then rely on the browser to replace the fake password with the actual password immediately before the web request is handed over to the operating system to be transmitted over the network. This removes the ability for malicious client-side scripts or browser extensions to access and exfiltrate the real password. We implement our design in the Firefox browser and conduct experiments, which show that it successfully thwarts malicious scripts and extensions on 97\% of the Alexa top 1000 websites, while also maintaining the capability to revert to default behavior on the remaining websites, avoiding functionality regressions. Most importantly, this design is transparent to users, requiring no change to user behavior.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LoginTrap: Uncovering Task-Agnostic Phishing-Style Indirect Prompt Injection Attacks against LLM-based Web Agents

    cs.CR 2026-08 conditional novelty 6.0 of 10

    LoginTrap induces LLM-based web agents to enter attacker-controlled login flows and submit sensitive information, achieving 86% average end-to-end success across LLM backbones.

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