The Security Awareness and Training (SAT) market exceeds multiple billion dollars annually, yet reliable data on organizational adoption remains scarce. Conflicting, survey-based figures from cybersecurity vendors leave researchers and decision-makers reliant on questionable insights. A new U.S. Securities and Exchange Commission (SEC) regulation, effective since late 2023, requires companies to disclose cybersecurity strategies in annual Form 10-K filings, offering a more consistent data source. In this study, we crawl and analyze filings from 5,286 U.S. companies across diverse sectors and sizes, using keyword searches and thematic analysis, which offers a lower-bound estimate of prevalent topics. We find that 78% of companies report implementing SAT and 27% conduct phishing simulations, with adoption varying significantly by sector and size. Larger companies report more extensive SAT efforts, often aligned with standards like NIST CSF. While multi-factor authentication (11%) is the most common employee-facing security control, many filings frame employees as a risk factor. Our findings help organizations critically assess SAT strategies and vendor claims, offer actionable insights for policymakers, and equip scholars with a coded dataset and crawling tools for ongoing longitudinal analysis.

Fallback authentication, the process of re-establishing access to an account when the primary authenticator is unavailable, holds critical significance. Approaches range from secondary channels like email and SMS to personal knowledge questions (PKQs) and social authentication. A key difference to primary authentication is that the duration between enrollment and authentication can be much longer, typically months or years. However, few systems have been studied over extended timeframes, making it difficult to know how well these systems truly help users recover their accounts. We also lack meaningful comparisons of schemes as most prior work examined two mechanisms at most. We report the results of a long-term user study of the usability of fallback authentication over 18 months to provide a fair comparison of the four most commonly used fallback authentication methods. We show that users prefer email and SMS-based methods, while mechanisms based on PKQs and trustees lag regarding successful resets and convenience.

Login notifications intend to inform users about sign-ins and help them protect their accounts from unauthorized access. Notifications are usually sent if a login deviates from previous ones, potentially indicating malicious activity. They contain information like the location, date, time, and device used to sign in. Users are challenged to verify whether they recognize the login (because it was them or someone they know) or to protect their account from unwanted access. In a user study, we explore users’ comprehension, reactions, and expectations of login notifications. We utilize two treatments to measure users’ behavior in response to notifications sent for a login they initiated or based on a malicious actor relying on statistical sign-in information. We find that users identify legitimate logins but need more support to halt malicious sign-ins. We discuss the identified problems and give recommendations for service providers to ensure usable and secure logins for everyone.

Data dashboards are designed to help users manage data collected about them. However, prior work showed that exposure to some dashboards, notably Google’s My Activity dashboard, results in significant decreases in perceived concern and increases in perceived benefit from data collection, contrary to expectations. We theorize that this result is due to the fact that data dashboards currently do not sufficiently “connect the dots” of the data food chain, that is, by connecting data collection with the use of that data. To evaluate this, we designed a study where participants assigned advertising interest labels to their own real activities, effectively acting as a behavioral advertising engine to “connect the dots.” When comparing pre- and post-labeling task responses, we find no significant difference in concern with Google’s data collection practices, which indicates that participants’ priors are maintained after more exposure to the data food chain (differing from prior work), suggesting that data dashboards that offer deeper perspectives of how data collection is used have potential. However, these gains are offset when participants are exposed to their true interest labels inferred by Google. Concern for data collection dropped significantly as participants viewed Google’s labeling as generic compared to their own more specific labeling. This presents a possible new paradox that must be overcome when designing data dashboards, the generic paradox, which occurs when users misalign individual, generic inferences from collected data as benign compared to the totality and specificity of many generic inferences made about them.

Mobile child care management applications can help child care facilities, preschools, and kindergartens to save time and money by allowing their employees to speed up everyday child care tasks using mobile devices. Such apps often allow child care workers to communicate with parents or guardians, sharing their children’s most private data (e. g., activities, photos, location, developmental aspects, and sometimes even medical information). To offer these services, child care apps require access to very sensitive data of minors that should never be shared over insecure channels and are subject to restrictive privacy laws. This work analyzes the privacy and security of 42 Android child care applications and their cloud-backends using a combination of static and dynamic analysis frameworks, configuration scanners, and inspecting their privacy policies. The results of our analysis show that while children do not use these apps, they can leak sensitive data about them. Alarming are the findings that many third-party (tracking) services are embedded in the applications and that adversaries can access personal data by abusing vulnerabilities in the applications. We hope our work will raise awareness about the privacy risks introduced by these applications and that regulatory authorities will focus more on these risks in the future.

In this article, we provide the first comprehensive study of user-chosen four- and six-digit PINs ( n =1705) collected on smartphones with participants being explicitly primed for device unlocking. We find that against a throttled attacker (with 10, 30, or 100 guesses, matching the smartphone unlock setting), using six-digit PINs instead of four-digit PINs provides little to no increase in security and surprisingly may even decrease security. We also study the effects of blocklists, where a set of “easy to guess” PINs is disallowed during selection. Two such blocklists are in use today by iOS, for four digits (274 PINs) as well as six digits (2,910 PINs). We extracted both blocklists and compared them with six other blocklists, three for each PIN length. In each case, we had a small (four-digit: 27 PINs; six-digit: 29 PINs), a large (four-digit: 2,740 PINs; six-digit: 291,000 PINs), and a placebo blocklist that always excluded the first-choice PIN. For four-digit PINs, we find that the relatively small blocklist in use today by iOS offers little to no benefit against a throttled guessing attack. Security gains are only observed when the blocklist is much larger. In the six-digit case, we were able to reach a similar security level with a smaller blocklist. As the user frustration increases with the blocklists size, developers should employ a blocklist that is as small as possible while ensuring the desired security. Based on our analysis, we recommend that for four-digit PINs a blocklist should contain the 1,000 most popular PINs to provide the best balance between usability and security and for six-digit PINs the 2,000 most popular PINs should be blocked.

We provide the first comprehensive study of user-chosen 4- and 6-digit PINs (n = 1220) collected on smartphones with participants being explicitly primed for device unlocking. We find that against a throttled attacker (with 10, 30, or 100 guesses, matching the smartphone unlock setting), using 6-digit PINs instead of 4-digit PINs provides little to no increase in security, and surprisingly may even decrease security. We also study the effects of blacklists, where a set of "easy to guess" PINs is disallowed during selection. Two such blacklists are in use today by iOS, for 4-digits (274 PINs) as well as 6-digits (2910 PINs). We extracted both blacklists compared them with four other blacklists, including a small 4-digit (27 PINs), a large 4-digit (2740 PINs), and two placebo blacklists for 4- and 6-digit PINs that always excluded the first-choice PIN. We find that relatively small blacklists in use today by iOS offer little or no benefit against a throttled guessing attack. Security gains are only observed when the blacklists are much larger, which in turn comes at the cost of increased user frustration. Our analysis suggests that a blacklist at about 10 % of the PIN space may provide the best balance between usability and security.

A rich literature has presented efficient techniques for estimating password strength by modeling password-cracking algorithms. Unfortunately, these previous techniques only apply to probabilistic password models, which real attackers seldom use. In this paper, we introduce techniques to reason analytically and efficiently about transformation-based password cracking in software tools like John the Ripper and Hashcat. We define two new operations, rule inversion and guess counting, with which we analyze these tools without needing to enumerate guesses. We implement these techniques and find orders-of-magnitude reductions in the time it takes to estimate password strength. We also present four applications showing how our techniques enable increased scientific rigor in optimizing these attacks' configurations. In particular, we show how our techniques can leverage revealed password data to improve orderings of transformation rules and to identify rules and words potentially missing from an attack configuration. Our work thus introduces some of the first principled mechanisms for reasoning scientifically about the types of password-guessing attacks that occur in practice.

Password strength meters are an important tool to help users choose secure passwords. Strength meters can only then provide reasonable guidance when they are accurate, i.e., their score correctly reflect password strength. A strength meter with low accuracy may do more harm than good and guide the user to choose passwords with a high score but low actual security. While a substantial number of different strength meters is proposed in the literature and deployed in practice, we are lacking a clear picture of which strength meters provide high accuracy, and thus are most helpful for guiding users. Furthermore, we lack a clear understanding of how to compare accuracies of strength meters. In this work, (i) we propose a set of properties that a strength meter needs to fulfill to be considered to have high accuracy, (ii) we use these properties to select a suitable measure that can determine the accuracy of strength meters, and (iii) we use the selected measure to compare a wide range of strength meters proposed in the academic literature, provided by password managers, operating systems, and those used on websites. We expect our work to be helpful in the selection of good password strength meters by service operators, and to aid the further development of improved strength meters.

Password reuse is widespread, so a breach of one provider's password database threatens accounts on other providers. When companies find stolen credentials on the black market and notice potential password reuse, they may require a password reset and send affected users a notification. Through two user studies, we provide insight into such notifications. In Study 1, 180 respondents saw one of six representative notifications used by companies in situations potentially involving password reuse. Respondents answered questions about their reactions and understanding of the situation. Notifications differed in the concern they elicited and intended actions they inspired. Concerningly, less than a third of respondents reported intentions to change any passwords. In Study 2, 588 respondents saw one of 15 variations on a model notification synthesizing results from Study 1. While the variations' impact differed in small ways, respondents' intended actions across all notifications would leave them vulnerable to future password-reuse attacks. We discuss best practices for password-reuse notifications and how notifications alone appear insufficient in solving password reuse.

Password vaults are used to store login credentials, usually encrypted by a master password, relieving the user from memorizing a large number of complex passwords. To manage accounts on multiple devices, vaults are often stored at an online service, which substantially increases the risk of leaking the (encrypted) vault. To protect the master password against guessing attacks, previous work has introduced cracking-resistant password vaults based on Honey Encryption. If decryption is attempted with a wrong master password, they output plausible-looking decoy vaults, thus seemingly disabling offline guessing attacks. In this work, we propose attacks against cracking-resistant password vaults that are able to distinguish between real and decoy vaults with high accuracy and thus circumvent the offered protection. These attacks are based on differences in the generated distribution of passwords, which are measured using Kullback-Leibler divergence. Our attack is able to rank the correct vault into the 1.3% most likely vaults (on median), compared to 37.8% of the best-reported attack in previous work. (Note that smaller ranks are better, and 50% is achievable by random guessing.) We demonstrate that this attack is, to a certain extent, a fundamental problem with all static Natural Language Encoders (NLE), where the distribution of decoy vaults is fixed. We propose the notion of adaptive NLEs and demonstrate that they substantially limit the effectiveness of such attacks. We give one example of an adaptive NLE based on Markov models and show that the attack is only able to rank the decoy vaults with a median rank of 35.1%.