Large-scale vulnerability notifications (VNs) can help hosting provider organizations (HPOs) identify and remediate security vulnerabilities that attackers can exploit in data breaches or phishing campaigns. Previous VN studies have primarily focused on factors under the control of reporters, such as sender reputation, email formatting, and communication channels. Despite these efforts, remediation rates for vulnerability notifications continue to remain consistently low. This paper presents the first in-depth study of how HPOs process vulnerability notifications internally and what organizational and operational factors influence VN effectiveness. We examine the problem from a different perspective to provide the first detailed understanding of the reasons behind persistently low remediation rates. Instead of manipulating parameters of VN campaigns, we interview hosting providers directly, investigating how they handle vulnerability notifications and what factors may influence VN effectiveness, such as VN awareness and reachability, HPOs’ service models, and perceived security risks. We conducted semi-structured interviews with 24 HPOs across shared hosting and web development services, representing varied company sizes and operator roles. Our findings reveal practical insights on VN processing and abuse workflows. While some providers remain hard to reach due to complex infrastructures, most report routinely handling VNs. However, limited remediation often stems from strict responsibility boundaries, where web application issues are seen as the customer’s domain. Low hosting fees and high volumes of daily compromises further discourage both proactive and reactive measures. Our findings show that HPOs blame negligent website owners, and prior works on website owners confirms they often undervalue their sites or lack security know-how. This misalignment raises further concerns about the efficacy of current VN approaches and whether they can reliably prompt remedial action under the existing operational model.

The DOM tree is a central part of modern web development, enabling JavaScript to interact with page content and structure. Only a few prior studies have studied its trustworthiness, despite its widespread use in guiding program logic and security decisions. Most notably, script gadgets have shown how this trust can be exploited by triggering the execution of benign JavaScript fragments with seemingly harmless markup injections. In this paper, we show that script gadgets are only the tip of the iceberg. Seemingly-benign markup injections can trigger the execution of fragments - that we call DOM gadgets - that, unlike script gadgets, do not necessarily result in a cross-site scripting vulnerability. Instead, they can result in a broader set of attacks, such as browser request hijacking attacks, cross-site request forgery attacks, and user interface manipulations.

Modern web applications use features like camera and geolocation for personalized experiences, requiring user permission via browser prompts.To explain these requests, applications provide rationales-contextual information on why permissions are needed.Despite their importance, little is known about how often rationales appear on the web or their influence on user decisions.This paper presents the first large-scale study of how the web ecosystem handles permission rationales, covering three areas: (i) identifying webpages that use permissions, (ii) detecting and classifying permission rationales, and (iii) analyzing their attributes to understand their impact on user decisions.We examined over 770K webpages from Chrome telemetry, finding 3.6K unique rationale texts and 749 rationale UIs across 85K pages.We extracted key rationale attributes and assessed their effect on user behavior by cross-referencing them with Chrome telemetry data.Our findings reveal nine key insights, providing the first evidence of how different rationales affect user decisions.

Crawlers are critical for ensuring the dependability and security of web applications by maximizing the code coverage of testing tools. Reinforcement learning (RL) has recently emerged as a promising approach to improve crawler exploration. However, existing approaches based on Q-learning face two major limitations: being state-based, they rely on brittle state abstractions that fail to generalize across diverse applications, and they employ rewards that prioritize underused actions but are not necessarily proportional to the improvement in code coverage. In this paper, we first substantiate the limitations of two popular Q-learning-based crawlers. We then propose Multi-Armed Krawler (MAK), a new crawler based on the Adversarial Multi-Armed Bandit problem. MAK is stateless and does not require the definition of brittle state abstractions that do not generalize to new web applications. By modeling the crawling process through a traditional graph abstraction and introducing an extrinsic reward correlated with code coverage, MAK compensates for the loss of expressiveness coming from its stateless nature. Our experimental results on public web applications show that MAK achieves greater coverage and faster convergence than its counterparts.

More and more immersive environments support third-party applications, leading to concerns about the trustworthiness of user interfaces (UIs), which attackers could exploit, endangering users. Although security warnings attempt to safeguard users by highlighting risks, most studies primarily target security indicators as usable intervention for app transitions in virtual reality. Our research broadens this focus by providing a systematic, data-driven investigation of security warnings for third-party applications in immersive environments. We analyzed a decade’s worth of top VR interactions and security conference findings and assessed the top 10 free VR applications from two leading stores each. From our design process, we implemented four warnings. Through two user studies involving 61 participants, we measured their responses to these warnings during virtual object interactions. Our findings indicate that a red glow on an object was the most effective warning, frequently associated with danger, while pop-up warnings were the least effective.

Scripting languages like Python or JavaScript are extremely popular among developers, in part due to their massive open-source ecosystems that enable smooth code reuse. However, recent work shows that a lot of scripting code runs C/C++ code under the hood, via native extensions. This might introduce subtle security issues that can surprise the users. Prior work in this domain relies on simple, intra-procedural, flow-insensitive data flow analysis to detect such problems, but it is unclear if a more holistic polyglot static analysis could be feasible, and if so, what are its costs and benefits.In this work, we propose CHARON, the first inter-procedural, polyglot static analysis for detecting vulnerabilities in scripting languages. Our approach advocates for linking together the code property graphs of the different languages and performing cross-language data flow analysis by switching between code representations, when cross-language function calls are encountered. In this way, CHARON supports data flows that cross several times the language boundary, spanning multiple functions on either side. We evaluated CHARON on 11.8K polyglot packages from npm and PyPI, containing 896M lines of code. CHARON identified 5,813 manually-confirmed, vulnerable data flows in 116 packages. We performed a baseline comparison of CHARON with single-language analysis on native code, showing a ~6x increase in true positives and ~4%, less false positive alerts. We demonstrated exploitability of the discovered vulnerabilities by creating 63 PoCs across 34 packages, showing, among others, how we can escalate a buffer overflow vulnerability in native extensions to arbitrary code execution, which we believe to be the first of its kind. Overall, our results show that inter-procedural, polyglot analysis is both feasible and effective for detection of native extension vulnerabilities.

There has been a rise in online platforms facilitating the buying and selling of social media accounts. While the trade of social media profiles is not inherently illegal, these platforms view such transactions as violations of their policies. They often take action against accounts involved in the misuse of platforms for financial gain. In this, we conduct a comprehensive analysis of marketplaces that enable the buying and selling of social media accounts.

Metaverses are virtual worlds where users can engage in social exchanges, collaborate, or play games. Their clients now are JavaScript programs that run inside modern web browsers. They implement functionalities typical of multiplayer video games, like 3D and physics engines, requiring them to maintain complex data structures of objects in the browser’s memory. Unfortunately, these objects can be accessed and manipulated by malicious users, allowing them to learn about events beyond the ones rendered on screen or to hijack the physics of the metaverse to spy on other users.In this paper, we propose one of the first comprehensive security assessments for web clients of metaverse platforms. We begin with a survey and selection of three metaverse platforms and introduce a software-centric threat modeling approach designed to identify the security-relevant entities. Then, we propose a JavaScript global object snapshot diffing technique to identify in-memory objects correlated with the attribute and design 10 attacks, of which eight successfully executed against at least one of the metaverses, enabling a malicious user to perform audio/video surveillance or continuous user position tracking — to mention a few — who could exacerbate current threats posed by stalkers and online abusers. Finally, we discuss the implications of our attacks should the metaverse become a business tool and possible solutions.

Clickbait PDFs, an entry point for multiple Web attacks, are distributed via SEO poisoning and rank high in search results due to being massively uploaded on abused or compromised websites. The central role of these hosts in the distribution of clickbait PDFs remains understudied, and it is unclear whether attackers differentiate the types of hosting for PDF uploads, how long they rely on hosts, and how affected parties respond to abuse. To address this, we conducted real-time analyses on hosts, collecting data on 4,648,939 clickbait PDFs served by 177,835 hosts over 17 months. Our results revealed a diverse infrastructure, with hosts falling into three main hosting types. We also identified at scale the presence of eight software components which facilitate file uploads and which are likely exploited for clickbait PDF distribution. We contact affected parties to report the misuse of their resources via a large-scale vulnerability notification. While we observed some effectiveness in terms of number of cleaned-up PDFs following the notification, long-term improvement in this infrastructure remained insignificant. This finding raises questions about the hosting providers' role in combating abuse and the actual impact of vulnerability notifications.

Request forgery attacks are among the oldest threats to Web applications, traditionally caused by server-side confused deputy vulnerabilities. However, recent advancements in client-side technologies have introduced more subtle variants of request forgery, where attackers exploit input validation flaws in client-side programs to hijack outgoing requests. We have little-to-no information about these client-side variants, their prevalence, impact, and countermeasures, and in this paper we undertake one of the first evaluations of the state of client-side request hijacking on the Web platform.Starting with a comprehensive review of browser API capabilities and Web specifications, we systematize request hijacking vulnerabilities and the resulting attacks, identifying 10 distinct vulnerability variants, including seven new ones. Then, we use our systematization to design and implement Sheriff, a static-dynamic tool that detects vulnerable data flows from attacker-controllable inputs to request-sending instructions. We instantiate Sheriff on the top of the Tranco top 10K sites, performing, to our knowledge, the first investigation into the prevalence of request hijacking flaws in the wild. Our study uncovers that request hijacking vulnerabilities are ubiquitous, affecting 9.6% of the top 10K sites. We demonstrate the impact of these vulnerabilities by constructing 67 proof-of-concept exploits across 49 sites, making it possible to mount arbitrary code execution, information leakage, open redirections and CSRF also against popular websites like Microsoft Azure, Starz, Reddit, and Indeed. Finally, we review and evaluate the adoption and efficacy of existing countermeasures against client-side request hijacking attacks, including browser-based solutions like CSP, COOP and COEP, and input validation.

Clickbait PDFs are PDF documents that do not embed malware but trick victims into visiting malicious web pages leading to attacks like password theft or drive-by download. While recent reports indicate a surge of clickbait PDFs, prior works have largely neglected this new threat, considering PDFs only as accessories of email phishing campaigns.

DOM Clobbering is a type of code-less injection attack where attackers insert a piece of non-script, seemingly benign HTML markup into a webpage and transform it to executable code by exploiting the unforeseen interactions between JavaScript code and the runtime environment. The attack techniques, browser behaviours, and vulnerable code patterns that enable DOM Clobbering has not been studied yet, and in this paper, we undertake one of the first evaluations of the state of DOM Clobbering on the Web platform. Starting with a comprehensive survey of existing literature and dynamic analysis of 19 different mobile and desktop browsers, we systematize DOM Clobbering attacks, uncovering 31.4K distinct markups that use five different techniques to unexpectedly overwrite JavaScript variables in at least one browser. Then, we use our systematization to identify and characterize program instructions that can be overwritten by DOM Clobbering, and use it to present TheThing, an automated system that detects clobberable data flows to security-sensitive instructions. We instantiate TheThing on the top of the Tranco top 5K sites, quantifying the prevalence and impact of DOM Clobbering in the wild. Our evaluation uncovers that DOM Clobbering vulnerabilities are ubiquitous, with a total of 9,467 vulnerable data flows across 491 affected sites, making it possible to mount arbitrary code execution, open redirections, or client-side request forgery attacks also against popular websites such as Fandom, Trello, Vimeo, TripAdvisor, WikiBooks and GitHub, that were not exploitable through the traditional attack vectors. Finally, in this paper, we also evaluate the robustness of the existing countermeasures, such as HTML sanitizers and Content Security Policy, against DOM Clobbering.

When browsing the web, none of us want sites to infer which other sites we may have visited before or are logged in to. However, attacker-controlled sites may infer this state through browser side-channels dubbed Cross-Site Leaks (XS-Leaks). Although these issues have been known since the 2000s, prior reports mostly found individual instances of issues rather than systematically studying the problem space. Further, actual impact in the wild often remained opaque.To address these open problems, we develop the first automated framework to systematically discover observation channels in browsers. In doing so, we detect and characterize 280 observation channels that leak information cross-site in the engines of Chromium, Firefox, and Safari, which include many variations of supposedly fixed leaks. Atop this framework, we create an automatic pipeline to find XS-Leaks in real-world websites. With this pipeline, we conduct the largest to-date study on XS-Leak prevalence in the wild by performing visit inference and a newly proposed variant cookie acceptance inference attack on the Tranco Top10K. In addition, we test 100 websites for the classic XS-Leak attack vector of login detection.Our results show that XS-Leaks pose a significant threat to the web ecosystem as at least 15%, 34%, and 77% of all tested sites are vulnerable to the three attacks. Also, we present substantial implementation differences between the browsers resulting in differing attack surfaces that matter in the wild. To ensure browser vendors and web developers alike can check their applications for XS-Leaks, we open-source our framework and include an extensive discussion on countermeasures to get rid of XS-Leaks in the near future and ensure new features in browsers do not introduce new XS-Leaks.

Chromium-based browsers now restrict cookies’ scope to a same-site context by changing the default policy for cookies, thus requiring developers to adapt their websites. The extent of the adoption and effectiveness of the SameSite policy has not been studied yet, and, in this paper, we undertake one of the first evaluations of the state of the SameSite cookie policy. We conducted a set of large-scale, longitudinal, both automated and manual measurements of the Alexa top 1K, 10K, 100K, and 500K sites across the main rollout dates of the SameSite policies, covering both SameSite usage and cross-site functionality breakage caused by the new default policy. Also, we performed an extensive evaluation of threats against the new Lax-by-default policy’s effectiveness, looking at the adequacy of the coverage provided by the Lax policy and bypass caused by website developers’ mistakes.Our study shows that the growth of sites using a SameSite policy has slowed down considerably after the enforcement dates. Then, the new Lax-by-default policy has affected about 19% of the functionalities implemented via cross-site requests without an explicit SameSite policy, most of which are for online ads. Third, our study observes a significant mismatch between the request contexts covered by Lax and the ones actually used by websites in the wild, making it possible to perform XS attacks also against popular websites such as Tumblr, Twitch, SoundCloud, Mailchimp, and Pixiv. Even when using Lax or Strict policies, much of their effectiveness depends on developers’ awareness of SameSite policies’ implications, who could introduce vulnerabilities or inconsistent policies, leading to SameSite policy bypasses. For example, we identified bypass in IMDB, Paypal, and Meetup. Also, we discovered a widespread SSO IdP abuse that attackers could use to attack target websites even when using stricter SameSite policies. Finally, in this paper, we also look at SameSite implementations in popular browsers and the default configuration in web frameworks.

Cross-Site Request Forgery (CSRF) is among the oldest web vulnerabilities that, despite its popularity and severity, it is still an understudied security problem. In this paper, we undertake one of the first security evaluations of CSRF defense as implemented by popular web frameworks, with the overarching goal to identify additional explanations to the occurrences of such an old vulnerability. Starting from a review of existing literature, we identify 16 CSRF defenses and 18 potential threats agains them. Then, we evaluate the source code of the 44 most popular web frameworks across five languages (i.e., JavaScript, Python, Java, PHP, and C#) covering about 5.5 million LoCs, intending to determine the implemented defenses and their exposure to the identified threats. We also quantify the quality of web frameworks’ documentation, looking for incomplete, misleading, or insufficient information required by developers to use the implemented CSRF defenses correctly.

Modern web applications are an integral part of our digital lives. As we put more trust in web applications, the need for security increases. At the same time, detecting vulnerabilities in web applications has become increasingly hard, due to the complexity, dynamism, and reliance on third-party components. Blackbox vulnerability scanning is especially challenging because (i) for deep penetration of web applications scanners need to exercise such browsing behavior as user interaction and asynchrony, and (ii) for detection of nontrivial injection attacks, such as stored cross-site scripting (XSS), scanners need to discover inter-page data dependencies.This paper illuminates key challenges for crawling and scanning the modern web. Based on these challenges we identify three core pillars for deep crawling and scanning: navigation modeling, traversing, and tracking inter-state dependencies. While prior efforts are largely limited to the separate pillars, we suggest an approach that leverages all three. We develop Black Widow, a blackbox data-driven approach to web crawling and scanning. We demonstrate the effectiveness of the crawling by code coverage improvements ranging from 63% to 280% compared to other crawlers across all applications. Further, we demonstrate the effectiveness of the web vulnerability scanning by featuring no false positives and finding more cross-site scripting vulnerabilities than previous methods. In older applications, used in previous research, we find vulnerabilities that the other methods miss. We also find new vulnerabili-ties in production software, including HotCRP, osCommerce, PrestaShop and WordPress.

Mobile applications (apps) have exploded in popularity, with billions of smartphone users using millions of apps available through markets such as the Google Play Store or the Apple App Store. While these apps have rich and useful functionality that is publicly exposed to end users, they also contain hidden behaviors that are not disclosed, such as backdoors and blacklists designed to block unwanted content. In this paper, we show that the input validation behavior-the way the mobile apps process and respond to data entered by users-can serve as a powerful tool for uncovering such hidden functionality. We therefore have developed a tool, InputScope, that automatically detects both the execution context of user input validation and also the content involved in the validation, to automatically expose the secrets of interest. We have tested InputScope with over 150,000 mobile apps, including popular apps from major app stores and preinstalled apps shipped with the phone, and found 12,706 mobile apps with backdoor secrets and 4,028 mobile apps containing blacklist secrets.

Perceptual ad-blocking is a novel approach that detects online advertisements based on their visual content. Compared to traditional filter lists, the use of perceptual signals is believed to be less prone to an arms race with web publishers and ad networks. We demonstrate that this may not be the case. We describe attacks on multiple perceptual ad-blocking techniques, and unveil a new arms race that likely disfavors ad-blockers. Unexpectedly, perceptual ad-blocking can also introduce new vulnerabilities that let an attacker bypass web security boundaries and mount DDoS attacks. We first analyze the design space of perceptual ad-blockers and present a unified architecture that incorporates prior academic and commercial work. We then explore a variety of attacks on the ad-blocker's detection pipeline, that enable publishers or ad networks to evade or detect ad-blocking, and at times even abuse its high privilege level to bypass web security boundaries. On one hand, we show that perceptual ad-blocking must visually classify rendered web content to escape an arms race centered on obfuscation of page markup. On the other, we present a concrete set of attacks on visual ad-blockers by constructing adversarial examples in a real web page context. For seven ad-detectors, we create perturbed ads, ad-disclosure logos, and native web content that misleads perceptual ad-blocking with 100% success rates. In one of our attacks, we demonstrate how a malicious user can upload adversarial content, such as a perturbed image in a Facebook post, that fools the ad-blocker into removing another users' non-ad content. Moving beyond the Web and visual domain, we also build adversarial examples for AdblockRadio, an open source radio client that uses machine learning to detects ads in raw audio streams.

Users regularly enter sensitive data, such as passwords, credit card numbers, or tax information, into the browser window. While modern browsers provide powerful client-side privacy measures to protect this data, none of these defenses prevent a browser compromised by malware from stealing it. In this work, we present Fidelius, a new architecture that uses trusted hardware enclaves integrated into the browser to enable protection of user secrets during web browsing sessions, even if the entire underlying browser and OS are fully controlled by a malicious attacker. Fidelius solves many challenges involved in providing protection for browsers in a fully malicious environment, offering support for integrity and privacy for form data, JavaScript execution, XMLHttpRequests, and protected web storage, while minimizing the TCB. Moreover, interactions between the enclave and the browser, the keyboard, and the display all require new protocols, each with their own security considerations. Finally, Fidelius takes into account UI considerations to ensure a consistent and simple interface for both developers and users. As part of this project, we develop the first open source system that provides a trusted path from input and output peripherals to a hardware enclave with no reliance on additional hypervisor security assumptions. These components may be of independent interest and useful to future projects. We implement and evaluate Fidelius to measure its performance overhead, finding that Fidelius imposes acceptable overhead on page load and user interaction for secured pages and has no impact on pages and page components that do not use its enhanced security features.

Security in the Internet has historically been added post-hoc, leaving services like email, which, after all, is used by 3.7 billion users, vulnerable to large-scale surveillance. For email alone, there is a multitude of proposals to mitigate known vulnerabilities, ranging from the introduction of completely new protocols to modifications of the communication paths used by big providers. Deciding which measures to deploy requires a deep understanding of the induced benefits, the cost and the resulting effects. This paper proposes the first automated methodology for making formal deployment assessments. Our planning algorithm analyses the impact and cost-efficiency of different known mitigation strategies against an attacker in a formal threat model. This novel formalisation of an infrastructure attacker includes routing, name resolution and application level weaknesses. We apply the methodology to a large-scale scan of the Internet, and assess how protocols like IPsec, DNSSEC, DANE, SMTP STS, SMTP over TLS and other mitigation techniques like server relocation can be combined to improve the confidentiality of email users in 45 combinations of attacker and defender countries and nine cost scenarios. This is the first deployment analysis for mitigation techniques at this scale.

Mobile apps are increasingly created using online application generators (OAGs) that automate app development, distribution, and maintenance. These tools significantly lower the level of technical skill that is required for app development, which makes them particularly appealing to citizen developers, i.e., developers with little or no software engineering background. However, as the pervasiveness of these tools increases, so does their overall influence on the mobile ecosystem's security, as security lapses by such generators affect thousands of generated apps. The security of such generated apps, as well as their impact on the security of the overall app ecosystem, has not yet been investigated. We present the first comprehensive classification of commonly used OAGs for Android and show how to fingerprint uniquely generated apps to link them back to their generator. We thereby quantify the market penetration of these OAGs based on a corpus of 2,291,898 free Android apps from Google Play and discover that at least 11.1% of these apps were created using OAGs. Using a combination of dynamic, static, and manual analysis, we find that the services' app generation model is based on boilerplate code that is prone to reconfiguration attacks in 7/13 analyzed OAGs. Moreover, we show that this boilerplate code includes well-known security issues such as code injection vulnerabilities and insecure WebViews. Given the tight coupling of generated apps with their services' backends, we further identify security issues in their infrastructure. Due to the blackbox development approach, citizen developers are unaware of these hidden problems that ultimately put the end-users sensitive data and privacy at risk and violate the user's trust assumption. A particular worrisome result of our study is that OAGs indeed have a significant amplification factor for those vulnerabilities, notably harming the health of the overall mobile app ecosystem.

Cross-Site Request Forgery (CSRF) vulnerabilities are a severe class of web vulnerabilities that have received only marginal attention from the research and security testing communities. While much effort has been spent on countermeasures and detection of XSS and SQLi, to date, the detection of CSRF vulnerabilities is still performed predominantly manually.

The Internet is built on top of intertwined network services, e.g., email, DNS, and content distribution networks operated by private or governmental organizations. Recent events have shown that these organizations may, knowingly or unknowingly, be part of global-scale security incidents including state-sponsored mass surveillance programs and large-scale DDoS attacks. For example, in March 2015 the Great Cannon attack has shown that an Internet service provider can weaponize millions of Web browsers and turn them into DDoS bots by injecting malicious JavaScript code into transiting TCP connections.

The Internet is an ever-growing ecosystem with diverse software and hardware applications deployed in numerous countries around the globe. This heterogenous structure, however, is reduced to a homogenous means of addressing servers, i.e., their IP address. Due to this, analyzing different Internet services for vulnerabilities at scale is easy, leading to many researcher focusing on large-scale detection of many types of flaws. On the other hand, the persons responsible for the administration of said services are as heterogenous as the Internet architecture itself: be it in spoken languages or knowledge of technical details of the services. The notification of vulnerable services has long been treated as a side note in research. Recently, the community has focussed more not only the detection of flaws, but also on the notification of affected parties. These works, however, only analyze a small segment of the problem space. Hence, in this paper, we investigate the issues encountered by the previous works and provide a number of future directions for research, ultimately aiming to allow for an easier means of notifying affected parties about vulnerabilities at scale.

Physically co-located virtual machines should be securely isolated from one another, as well as from the underlying layers in a virtualized environment. In particular the virtualized environment is supposed to guarantee the impossibility of an adversary to attack a virtual machine e.g., by exploiting a side-channel stemming from the usage of shared physical or software resources. However, this is often not the case and the lack of sufficient logical isolation is considered a key concern in virtualized environments. In the academic world this view has been reinforced during the last years by the demonstration of sophisticated side-channel attacks (SCAs). In this paper we argue that the feasibility of executing a SCA strongly depends on the actual context of the execution environment. To reflect on these observations, we propose a feasibility assessment framework for SCAs using cache based systems as an example scenario. As a proof of concept we show that the feasibility of cache-based side-channel attacks can be assessed following the proposed approach.