To encourage users to use stronger and more secure passwords, modern web browsers offer users password management services, allowing users to save previously entered passwords locally onto their hard drives. We present Lupin, a tool that automatically extracts these saved passwords without the user's knowledge. Lupin allows a network adversary to obtain passwords as long as the login form appears on a non-HTTPS page. Unlike existing password sniffing tools, Lupin can obtain passwords for websites users are not visiting. Furthermore, Lupin can extract passwords embedded in login forms with a destination address served in HTTPS. To determine the number of websites vulnerable to our attack, we crawled the top 45,000 most popular websites from Alexa's top website list and discovered that at least 28 of these sites are vulnerable. To further demonstrate the feasibility of our attack, we tested Lupin under controlled conditions using one of the authors' computers. Lupin was able to extract passwords from 1,000 websites in less than 35 seconds. We suggest techniques for web developers to protect their web applications from attack, and we propose alternative designs for a secure password manager.
Several researchers have attempted to design a secure password manager. However, none of them has considered the effect of a network attacker. PwdHash transparently produces a different password for each site by using cryptographic hash functions @cite_7 , hence preventing a web attacker from compromising multiple accounts from the same user using the same password. Passpet aims to protect the user's login credentials from phishing attackers by associating each trusted website with user-assigned labels @cite_1 . Internet users' password strength, as well as their password management habits, has also been extensively studied in previous literature @cite_2 @cite_0 @cite_8 @cite_4 . Most of the existing research has found that the majority of passwords on the Internet are weak and that users tend to reuse existing passwords. The attack described in our work does not target the weaknesses of these web passwords, but rather, it exploits a vulnerability in the design of several commercial password managers.
@cite_4 @cite_7 @cite_8 @cite_1 @cite_0 @cite_2
2148327104 2129768690 2030993695 2048755632
We describe Passpet, a tool that improves both the convenience and security of website logins through a combination of techniques. Password hashing helps users manage multiple accounts by turning a single memorized password into a different password for each account. User-assigned site labels (petnames) help users securely identify sites in the face of determined attempts at impersonation (phishing). Password-strengthening measures defend against dictionary attacks. Customizing the user interface defends against user-interface spoofing attacks. We propose new improvements to these techniques, discuss how they are integrated into a single tool, and compare Passpet to other solutions for managing passwords and preventing phishing. We present a usability study of two recent password manager proposals: PwdHash (, 2005) and Password Multiplier (, 2005). Both papers considered usability issues in greater than typical detail, the former briefly reporting on a small usability study; both also provided implementations for download. Our study involving 26 users found that both proposals suffer from major usability problems. Some of these are not "simply" usability issues, but rather lead directly to security exposures. Not surprisingly, we found the most significant problems arose from users having inaccurate or incomplete mental models of the software. Our study revealed many interesting misunderstandings D for example, users reporting a task as easy even when unsuccessful at completing that task; and believing their passwords were being strengthened when in fact they had failed to engage the appropriate protection mechanism. Our findings also suggested that ordinary users would be reluctant to optin to using these managers: users were uncomfortable with "relinquishing control" of their passwords to a manager, did not feel that they needed the password managers, or that the managers provided greater security. Computer users are asked to generate, keep secret, and recall an increasing number of passwords for uses including host accounts, email servers, e-commerce sites, and online financial services. Unfortunately, the password entropy that users can comfortably memorize seems insufficient to store unique, secure passwords for all these accounts, and it is likely to remain constant as the number of passwords (and the adversary's computational power) increases into the future. In this paper, we propose a technique that uses a strengthened cryptographic hash function to compute secure passwords for arbitrarily many accounts while requiring the user to memorize only a single short password. This mechanism functions entirely on the client; no server-side changes are needed. Unlike previous approaches, our design is both highly resistant to brute force attacks and nearly stateless, allowing users to retrieve their passwords from any location so long as they can execute our program and remember a short secret. This combination of security and convenience will, we believe, entice users to adopt our scheme. We discuss the construction of our algorithm in detail, compare its strengths and weaknesses to those of related approaches, and present Password Multiplier, an implementation in the form of an extension to the Mozilla Firefox web browser. We report on the largest corpus of user-chosen passwords ever studied, consisting of anonymized password histograms representing almost 70 million Yahoo! users, mitigating privacy concerns while enabling analysis of dozens of subpopulations based on demographic factors and site usage characteristics. This large data set motivates a thorough statistical treatment of estimating guessing difficulty by sampling from a secret distribution. In place of previously used metrics such as Shannon entropy and guessing entropy, which cannot be estimated with any realistically sized sample, we develop partial guessing metrics including a new variant of guesswork parameterized by an attacker's desired success rate. Our new metric is comparatively easy to approximate and directly relevant for security engineering. By comparing password distributions with a uniform distribution which would provide equivalent security against different forms of guessing attack, we estimate that passwords provide fewer than 10 bits of security against an online, trawling attack, and only about 20 bits of security against an optimal offline dictionary attack. We find surprisingly little variation in guessing difficulty; every identifiable group of users generated a comparably weak password distribution. Security motivations such as the registration of a payment card have no greater impact than demographic factors such as age and nationality. Even proactive efforts to nudge users towards better password choices with graphical feedback make little difference. More surprisingly, even seemingly distant language communities choose the same weak passwords and an attacker never gains more than a factor of 2 efficiency gain by switching from the globally optimal dictionary to a population-specific lists.