Web-based behavior-manipulation attacks (BMAs)—such as scareware, fake software downloads, tech support scams, etc.—are a class of social engineering (SE) attacks that exploit human decision-making vulnerabilities. These attacks remain under-studied compared to other attacks such as information harvesting attacks (e.g., phishing) or malware infections. Prior technical work has primarily focused on measuring BMAs, offering little in the way of generic defenses. To address this gap, we introduce Pixel Patrol 3D (PP3D), the first end-to-end browser framework for discovering, detecting, and defending against behavior-manipulating SE attacks in real time. PP3D consists of a visual detection model implemented within a browser extension, which deploys the model client-side to protect users across desktop and mobile devices while preserving privacy. Our evaluation shows that PP3D can achieve above 99% detection rate at 1% false positives, while maintaining good latency and overhead performance across devices. Even when faced with new BMA samples collected months after training the detection model, our defense system can still achieve above 97% detection rate at 1% false positives. These results demonstrate that our framework offers a practical, effective, and generalizable defense against a broad and evolving class of web behavior-manipulation attacks.

Canvas fingerprinting has become one of the most effective techniques for tracking users online, allowing websites to identify and track visitors without their consent. In this paper, we investigate four primary defense techniques designed to counter canvas fingerprinting, systematically analyzing their adoption across 18 browser extensions in Chrome and Firefox, as well as built-in protections from five major browsers: Chrome, Firefox, Brave, Tor, and Safari. Our analysis reveals significant disparities in the implementation and effectiveness of these defenses, with randomization-based techniques being the most widely adopted, particularly across nine extensions and in the privacy-focused browser, Brave. Despite their sophistication, we demonstrate successful attacks on all these randomization mechanisms, revealing that their supposed non-deterministic behavior can, in fact, be predicted and exploited. In summary, we demonstrate that, unfortunately, no fully deployable defense against canvas fingerprinting attacks exists currently. We conclude by proposing recommendations to strengthen existing defenses and enhance their resistance to future attacks.

The mainstream adoption of cryptocurrencies has led to a surge in wallet-related issues reported by ordinary users on social media platforms. In parallel, there is an increase in an emerging fraud trend called cryptocurrency-based technical support scam, in which fraudsters offer fake wallet recovery services and target users experiencing wallet-related issues.In this paper, we perform a comprehensive study of cryptocurrency-based technical support scams. We present an analysis apparatus called HoneyTweet to analyze this kind of scam. Through HoneyTweet, we lure over 9K scammers by posting 25K fake wallet support tweets (so-called honey tweets). We then deploy automated systems to interact with scammers to analyze their modus operandi. In our experiments, we observe that scammers use Twitter as a starting point for the scam, after which they pivot to other communication channels (e.g., email, Instagram, or Telegram) to complete the fraud activity. We track scammers across those communication channels and bait them into revealing their payment methods. Based on the modes of payment, we uncover two categories of scammers that either request secret key phrase submissions from their victims or direct payments to their digital wallets. Furthermore, we obtain scam confirmation by deploying honey wallet addresses and validating private key theft. We also collaborate with the prominent payment service provider by sharing scammer data collections. The payment service provider feedback was consistent with our findings, thereby supporting our methodology and results. By consolidating our analysis across various vantage points, we provide an end-to-end scam lifecycle analysis and propose recommendations for scam mitigation.

In this paper, we design and implement C-Frame, the first measurement system to collect real-time, in-the-wild data on modern CAPTCHA attacks. For this, we study the recent evolution in the protocols of CAPTCHAs as well as human-driven farms that facilitate attacks against CAPTCHAs. This study leads us directly to the discovery of a unique vantage point to conduct a global-scale CAPTCHA attack measurement study. Harnessing this, we design and build C-Frame to be CAPTCHA-agnostic and ethically considerate. We then deploy our system for a 92-day period resulting in capturing of 425,257 CAPTCHA attacks on 1417 sites.In order to characterize these attacks, we leverage a carefully designed qualitative analysis approach using 3 analysts. Our study results in delineation of 34 different CAPTCHA-attack categories with several interesting real world attack examples. Twitter received the largest number of CAPTCHA attacks overall (about 255,480 attack requests) most of which attempt to create bot accounts. We also categorized and captured attacks such as ticket scalping attempts (e.g. a Taylor Swift concert event in Brazil), fraudulent lawsuit claims, and abusive appointment booking attempts (e.g. a Spain visa site in China). We also found CAPTCHA-assisted attempts to download data from government website (e.g. websites of 20 US states). We ascribe our attacks to 58 different countries across 5 continents. We present a detailed measurement analysis to give insights on this attack data and also suggest some future potential remediation measures that can be inspired by our system.

Technical support scams (TSS) are social engineering attacks that aim to exploit users that have limited knowledge about technology, such as the elderly, causing significant financial loss to vulnerable citizens. The security community has attempted to respond to these web-based scams with different countermeasures. However, to the best of our knowledge, no robust countermeasures have been proposed thus far to defend against modern TSS campaigns that abuse web search engines to inflate their rankings in search results and lure many potential victims.To defend against these TSS attacks, in this paper we first study the TSS ecosystem, with particular focus on how modern TSS campaigns are operated and promoted on the web. Then, we capitalize on our findings by proposing a novel detection system named TASR that can be used to differentiate TSS websites from legitimate technical support websites in a topic-agnostic way, by leveraging features that capture key traits of how TSS web pages are promoted. Our cross-validation tests show that TASR can detect 94.5% of the TSS links in web search results at a false positive rate of less than 1%, significantly outperforming previous work.

We conduct the first systematic study of the effectiveness of Web Audio API-based browser fingerprinting mechanisms and present new insights. First, we show that audio fingerprinting vectors, unlike other prior vectors, reveal an apparent fickleness with some users' browsers giving away differing fingerprints in repeated attempts. However, we show that it is possible to devise a graph-based analysis mechanism to collectively consider all the different fingerprints left by users' browsers and thus craft a highly stable fingerprinting mechanism. Next, we investigate the diversity of audio fingerprints and compare this with prior fingerprinting techniques. Our results show that audio fingerprints are much less diverse than other vectors with only 95 distinct fingerprints among 2093 users. At the same time, further analysis shows that web audio fingerprinting can potentially bring considerable additive value to existing fingerprinting mechanisms. For instance, our results show that the addition of web audio fingerprinting causes a 9.6% increase in entropy when compared to using Canvas fingerprinting alone. We also show that our results contradict the current security and privacy recommendations provided by W3C regarding audio fingerprinting.

Despite phishing attacks and detection systems being extensively studied, phishing is still on the rise and has recently reached an all-time high. Attacks are becoming increasingly sophisticated, leveraging new web design patterns to add perceived legitimacy and, at the same time, evade state-of-the-art detectors and web security crawlers.

The rapid growth of online advertising has fueled the growth of ad-blocking software, such as new ad-blocking and privacy-oriented browsers or browser extensions. In response, both ad publishers and ad networks are constantly trying to pursue new strategies to keep up their revenues. To this end, ad networks have started to leverage the Web Push technology enabled by modern web browsers. As web push notifications (WPNs) are relatively new, their role in ad delivery has not been yet studied in depth. Furthermore, it is unclear to what extent WPN ads are being abused for malvertising (i.e., to deliver malicious ads). In this paper, we aim to fill this gap. Specifically, we propose a system called PushAdMiner that is dedicated to (1) automatically registering for and collecting a large number of web-based push notifications from publisher websites, (2) finding WPN-based ads among these notifications, and (3) discovering malicious WPN-based ad campaigns. Using PushAdMiner, we collected and analyzed 21,541 WPN messages by visiting thousands of different websites. Among these, our system identified 572 WPN ad campaigns, for a total of 5,143 WPN-based ads that were pushed by a variety of ad networks. Furthermore, we found that 51% of all WPN ads we collected are malicious, and that traditional ad-blockers and malicious URL filters are remarkably ineffective against WPN-based malicious ads, leaving a significant abuse vector unchecked.

Malicious ads often use social engineering (SE) tactics to coax users into downloading unwanted software, purchasing fake products or services, or giving up valuable personal information. These ads are often served by low-tier ad networks that may not have the technical means (or simply the will) to patrol the ad content they serve to curtail abuse.

In an attempt to coerce useful information about the behavior of new malware families, threat analysts commonly force newly collected malicious software samples to run within a sandboxed environment. The main goal is to gather intelligence that can later be leveraged to detect and enumerate new malware infections within a network. Currently, most analysis environments "blindly" execute each newly collected malware sample for a predetermined amount of time (e.g., four to five minutes). However, a large majority of malware samples that are forced through sandbox execution are simply repackaged versions of previously seen (and already analyzed) malware. Consequently, a significant amount of time may be wasted in analyzing samples that do not generate new intelligence.