Secure Web Applications Group

Given the success of the Web platform, attackers have abused its main programming language, namely JavaScript, to mount different types of attacks on their victims. Due to the large volume of such malicious scripts, detection systems rely on static analyses to quickly process the vast majority of samples. These static approaches are not infallible though and lead to misclassifications. Also, they lack semantic information to go beyond purely syntactic approaches. In this paper, we propose JStap, a modular static JavaScript detection system, which extends the detection capability of existing lexical and AST-based pipelines by also leveraging control and data flow information. Our detector is composed of ten modules, including five different ways of abstracting code, with differing levels of context and semantic information, and two ways of extracting features. Based on the frequency of these specific patterns, we train a random forest classifier for each module. In practice, JStap outperforms existing systems, which we reimplemented and tested on our dataset totaling over 270,000 samples. To improve the detection, we also combine the predictions of several modules. A first layer of unanimous voting classifies 93% of our dataset with an accuracy of 99.73%, while a second layer–based on an alternative modules’ combination–labels another 6.5% of our initial dataset with an accuracy over 99%. This way, JStap can be used as a precise pre-filter, meaning that it would only need to forward less than 1% of samples to additional analyses. For reproducibility and direct deployability of our modules, we make our system publicly available.

In the malware field, learning-based systems have become popular to detect new malicious variants. Nevertheless, it has been shown that attackers with specific and internal knowledge of a target system may be able to produce input samples which are misclassified. In practice, the assumption of strong attackers is not realistic as it implies access to insider information. We instead propose HideNoSeek, a novel and generic camouflage attack, which evades the entire class of detectors based on syntactic features, without needing any information about the system it is trying to evade. Our attack consists of changing the constructs of a malicious JavaScript sample to imitate a benign syntax. In particular, HideNoSeek uses malicious seeds and searches for similarities at the Abstract Syntax Tree (AST) level between the seeds and traditional benign scripts. Specifically, it replaces benign sub-ASTs by identical malicious ones and adjusts the benign data dependencies–without changing the AST–, so that the malicious semantics is kept after execution. In practice, we are able to generate 91,020 malicious scripts from 22 malicious seeds and 8,279 benign web pages. In addition, we can hide on average 14 malicious samples in a benign AST of the Alexa top 10, and 13 in each of the five most popular JavaScript libraries. In particular, a standard trained classifier has over 99.7% false-negatives with HideNoSeek inputs, while a classifier trained on such samples has over 96% false-positives, rendering the targeted static detectors unreliable.

The direct client-side inclusion of cross-origin JavaScript resources in Web applications is a pervasive practice to consume third-party services and to utilize externally provided libraries. The downside of this practice is that such external code runs in the same context and with the same privileges as the first-party code. Thus, all potential security problems in the code directly affect the including site. To explore this problem, we present an empirical study which shows that more than 25% of all sites affected by Client-Side Cross-Site Scripting are only vulnerable due to a flaw in the included third-party code. Motivated by this finding, we propose ScriptProtect, a non-intrusive transparent protective measure to address security issues introduced by external script resources. ScriptProtect automatically strips third-party code from the ability to conduct unsafe string-to-code conversions. Thus, it effectively removes the root-cause of Client-Side XSS without affecting first-party code in this respective. As ScriptProtect is realized through a lightweight JavaScript instrumentation, it does not require changes to the browser and only incurs a low runtime overhead of about 6%. We tested its compatibility on the Alexa Top 5,000 and found that 30% of these sites could benefit from ScriptProtect’s protection today without changes to their application code.

The Web has become highly interactive and an important driver for modern life, enabling information retrieval, social exchange, and online shopping. From the security perspective, Cross-Site Scripting (XSS) is one of the most nefarious attacks against Web clients. Research has long since focussed on three categories of XSS: reflected, persistent, and DOM-based XSS. In this paper, we argue that our community must consider at least four important classes of XSS and present the first systematic study of the threat of Persistent Client-Side XSS, caused by the insecure usage of client-side storages. While the existence of this class has been acknowledged, especially by the non-academic community like OWASP, prior works have either only found such flaws as side effects of other analyses or focused on a limited set of applications to analyze. Therefore, the community lacks in-depth knowledge about the actual prevalence of Persistent Client-Side XSS in the wild. To close this research gap, we leverage taint tracking to identify suspicious flows from client-side persistent storage (Web Storage, cookies) to dangerous sinks (HTML, JavaScript, and script.src). We discuss two attacker models capable of injecting malicious payloads into these storages, i.e., a network attacker capable of temporarily hijacking HTTP communication (e.g., in a public WiFi), and a Web attacker who can leverage flows into storage or an existing reflected XSS flaw to persist their payload. With our taint-aware browser and these models in mind, we study the prevalence of Persistent Client-Side XSS in the Alexa Top 5,000 domains. We find that more than 8% of them have unfiltered data flows from persistent storages to a dangerous sink, which showcases the developers’ inherent trust in the integrity of storage content. Even worse, if we only consider sites that make use of data originating from storages, 21% of the sites are vulnerable. For those sites with vulnerable flaws from storage to sink, we find that at least 70% are directly exploitable by our attacker models. Finally, investigating the vulnerable flows originating from storages allows us to categorize them into four disjoint categories and propose appropriate mitigations.

JavaScript is a browser scripting language initially created to enhance the interactivity of web sites and to improve their user-friendliness. However, as it offloads the work to the user’s browser, it can be used to engage in malicious activities such as Crypto-Mining, Drive-by-Download attacks, or redirections to web sites hosting malicious software. Given the prevalence of such nefarious scripts, the anti-virus industry has increased the focus on their detection. The attackers, in turn, make increasing use of obfuscation techniques, so as to hinder analysis and the creation of corresponding signatures. Yet these malicious samples share syntactic similarities at an abstract level, which enables to bypass obfuscation and detect even unknown malware variants. In this paper, we present JaSt, a low-overhead solution that combines the extraction of features from the abstract syntax tree with a random forest classifier to detect malicious JavaScript instances. It is based on a frequency analysis of specific patterns, which are either predictive of benign or of malicious samples. Even though the analysis is entirely static, it yields a high detection accuracy of almost 99.5% and has a low false-negative rate of 0.54%.

After treating the notification of affected parties as mere side-notes in research, our community has recently put more focus on how vulnerability disclosure can be conducted at scale. The first works in this area have shown that while notifications are helpful to a significant fraction of operators, the vast majority of systems remain unpatched. In this paper, we build on these previous works, aiming to understand why the effects are not more significant. To that end, we report on a notification experiment targeting more than 24,000 domains, which allowed us to analyze what technical and human aspects are roadblocks to a successful campaign. As part of this experiment, we explored potential alternative notification channels beyond email, including social media and phone. In addition, we conducted an anonymous survey with the notified operators, investigating their perspectives on our notifications. We show the pitfalls of email-based communications, such as the impact of anti-spam filters, the lack of trust by recipients, and hesitations to fix vulnerabilities despite awareness. However, our exploration of alternative communication channels did not suggest a more promising medium. Seeing these results, we pinpoint future directions in improving security notifications.

The Web today is a growing universe of pages and applications teeming with interactive content. The security of such applications is of the utmost importance, as exploits can have a devastating impact on personal and economic levels. The number one programming language in Web applications is PHP, powering more than 80% of the top ten million websites. Yet it was not designed with security in mind, and, today, bears a patchwork of fixes and inconsistently designed functions with often unexpected and hardly predictable behavior that typically yield a large attack surface. Consequently, it is prone to different types of vulnerabilities, such as SQL Injection or Cross-Site Scripting. In this paper, we present an interprocedural analysis technique for PHP applications based on code property graphs that scales well to large amounts of code and is highly adaptable in its nature. We implement our prototype using the latest features of PHP 7, leverage an efficient graph database to store code property graphs for PHP, and subsequently identify different types of Web application vulnerabilities by means of programmable graph traversals. We show the efficacy and the scalability of our approach by reporting on an analysis of 1,854 popular open-source projects, comprising almost 80 million lines of code.

While in its early days, the Web was mostly static, it has organically grown into a full-fledged technology stack. This evolution has not followed a security blueprint, resulting in many classes of vulnerabilities specific to the Web. Even though the server-side code of the past has long since vanished, the Internet Archive gives us a unique view on the historical development of the Web’s client side and its (in)security. Uncovering the insights which fueled this development bears the potential to not only gain a historical perspective on client-side Web security, but also to outline better practices going forward. To that end, we examined the code and header information of the most important Web sites for each year between 1997 and 2016, amounting to 659,710 different analyzed Web documents. From the archived data, we first identify key trends in the technology deployed on the client, such as the increasing complexity of client-side Web code and the constant rise of multi-origin appli- cation scenarios. Based on these findings, we then assess the advent of corresponding vulnerability classes, investigate their prevalence over time, and analyze the security mechanisms developed and deployed to mitigate them. Correlating these results allows us to draw a set of overarching conclusions: Along with the dawn of JavaScript-driven applications in the early years of the millennium, the likelihood of client-side injection vulnerabilities has risen. Furthermore, there is a noticeable gap in adoption speed between easy-to-deploy security headers and more involved measures such as CSP. But there is also no evidence that the usage of the easy-to- deploy techniques reflects on other security areas. On the contrary, our data shows for instance that sites that use HTTPonly cookies are actually more likely to have a Cross-Site Scripting problem. Finally, we observe that the rising security awareness and introduction of dedicated security technologies had no immediate impact on the overall security of the client-side Web.

One of the major disturbances for network providers in recent years have been Distributed Reflective Denial-of-Service (DRDoS) attacks. In such an attack, the attacker spoofs the IP address of a victim and sent a flood of tiny packets to vulnerable services which then respond with much larger replies to the victim. Led by the idea that the attacker cannot fabricate the number of hops between the amplifier and the victim, Hop Count Filtering (HCF) mechanisms that analyze the Time to Live of incoming packets have been proposed as a solution. In this paper, we evaluate the feasibility of using Hop Count Filtering to mitigate DRDoS attacks. To that end, we detail how a server can use active probing to learn TTLs of alleged packet senders. Based on data sets of benign and spoofed NTP requests, we find that a TTL-based defense could block over 75% of spoofed traffic, while allowing 85% of benign traffic to pass. To achieve this performance, however, such an approach must allow for a tolerance of +/-2 hops. Motivated by this, we investigate the tacit assumption that an attacker cannot learn the correct TTL value. By using a combination of tracerouting and BGP data, we build statistical models which allow to estimate the TTL within that tolerance level. We observe that by wisely choosing the used amplifiers, the attacker is able to circumvent such TTL-based defenses. Finally, we argue that any (current or future) defensive system based on TTL values can be bypassed in a similar fashion, and find that future research must be steered towards more fundamental solutions to thwart any kind of IP spoofing attacks.

In recent years, the drive-by malware space has undergone significant consolidation. Today, the most common source of drive-by downloads are socalled exploit kits (EKs). This paper presents Kizzle, the first prevention technique specifically designed for finding exploit kits. Our analysis shows that while the JavaScript delivered by kits varies greatly, the unpacked code varies much less, due to the kits authors’ code reuse between versions. Ironically, this well-regarded software engineering practice allows us to build a scalable and precise detector that is able to quickly respond to superficial but frequent changes in EKs. Kizzle is able to generate anti-virus signatures for detecting EKs, which compare favorably to manually created ones. Kizzle is highly responsive and can generate new signatures within hours. Our experiments show that Kizzle produces high-accuracy signatures. When evaluated over a four-week period, false-positive rates for Kizzle are under 0.03%, while the false-negative rates are under 5%.

Large-scale discovery of thousands of vulnerable Web sites has become a frequent event, thanks to recent advances in security research and the rise in maturity of Internet-wide scanning tools. The issues related to disclosing the vulnerability information to the affected parties, however, have only been treated as a side note in prior research. In this paper, we systematically examine the feasibility and efficacy of large-scale notification campaigns. For this, we comprehensively survey existing communication channels and evaluate their usability in an automated notification process. Using a data set of over 44,000 vulnerable Web sites, we measure success rates, both with respect to the total number of fixed vulnerabilities and to reaching responsible parties, with the following high-level results: Although our campaign had a statistically significant impact compared to a control group, the increase in the fix rate of notified domains is marginal. If a notification report is read by the owner of the vulnerable application, the likelihood of a subsequent resolution of the issues is sufficiently high: about 40%. But, out of 35,832 transmitted vulnerability reports, only 2,064 (5.8%) were actually received successfully, resulting in an unsatisfactory overall fix rate, leaving 74.5% of Web applications exploitable after our month-long experiment. Thus, we conclude that currently no reliable notification channels exist, which significantly inhibits the success and impact of large-scale notification.

Modern Web sites frequently generate JavaScript on-the- fly via server-side scripting, incorporating personalized user data in the process. In general, cross-domain access to such sensitive resources is prevented by the Same-Origin Policy. The inclusion of remote scripts via the HTML script tag, however, is exempt from this policy. This exemption allows an adversary to import and execute dynamically generated scripts while a user visits an attacker-controlled Web site. By observing the execution behavior and the side effects the inclusion of the dynamic script causes, the attacker is able to leak private user data leading to severe consequences ranging from privacy violations up to full compromise of user accounts. Although this issues has been known for several years under the term Cross-Site Script Inclusion, it has not been analyzed in-depth on the Web. Therefore, to systematically investigate the issue, we conduct a study on its prevalence in a set of 150 top-ranked domains. We observe that a third of the surveyed sites utilize dynamic JavaScript. After evaluating the effectiveness of the deployed countermeasures, we show that more than 80% of the sites are susceptible to attacks via remote script inclusion. Given the results of our study, we provide a secure and functionally equivalent alternative to the use of dynamic scripts.

Although studies have shown that at least one in ten Web pages contains a client-side XSS vulnerability, the prevalent causes for this class of Cross-Site Scripting have not been studied in depth. Therefore, in this paper, we present a large-scale study to gain insight into these causes. To this end, we analyze a set of 1,273 real-world vulnerabilities contained on the Alexa Top 10k domains using a specifically designed architecture, consisting of an infrastructure which allows us to persist and replay vulnerabilities to ensure a sound analysis. In combination with a taint-aware browsing engine, we can therefore collect important execution trace information for all flaws. Based on the observable characteristics of the vulnerable JavaScript, we derive a set of metrics to measure the complexity of each flaw. We subsequently classify all vulnerabilities in our data set accordingly to enable a more systematic analysis. In doing so, we find that although a large portion of all vulnerabilities have a low complexity rating, several incur a significant level of complexity and are repeatedly caused by vulnerable third-party scripts. In addition, we gain insights into other factors related to the existence of client-side XSS flaws, such as missing knowledge of browser-provided APIs, and find that the root causes for Client-Side Cross-Site Scripting range from unaware developers to incompatible first- and third-party code.

Cross-site Scripting (XSS) ist eine weit verbreitete Verwundbarkeitsklasse in Web-Anwendungen und kann sowohl von server-seitigem als auch von client-seitigem Code verursacht werden. Allerdings wird XSS primaer als ein server-seitiges Problem wahrgenommen, motiviert durch das Offenlegen von zahlreichen entsprechenden XSS-Schwachstellen. In den letzten Jahren kann jedoch eine zunehmende Verlagerung von Anwendungslogik in den Browser beobachtet werden; eine Entwicklung, die im Rahmen des sogenannten Web 2.0 begonnen hat. Dies legt die Vermutung nahe, dass auch client-seitiges XSS an Bedeutung gewinnen koennte. In diesem Beitrag stellen wir eine umfassende Studie vor, in der wir, mittels eines voll-automatisierten Ansatzes, die fuehrenden 5000 Webseiten des Alexa Indexes auf DOM-basiertes XSS untersucht haben. Im Rahmen dieser Studie, konnten wir 6.167 derartige Verwundbarkeiten identifizieren, die sich auf 480 der untersuchten Anwendungen verteilen.

To ease the burden of repeated password authentication on multiple sites, modern Web browsers provide password managers, which offer to automatically complete password fields on Web pages, after the password has been stored once. Unfortunately, these managers operate by simply inserting the clear-text password into the document’s DOM, where it is accessible by JavaScript. Thus, a successful Cross-site Scripting attack can be leveraged by the attacker to read and leak password data which has been provided by the password manager. In this paper, we assess this potential threat through a thorough survey of the current password manager generation and observable characteristics of password fields in popular Web sites. Furthermore, we propose an alternative password manager design, which robustly prevents the identified attacks, while maintaining compatibility with the established functionality of the existing approaches.

The current generation of client-side Cross-Site Scripting filters rely on string comparison to detect request values that are reflected in the corresponding response’s HTML. This coarse approximation of occurring data flows is incapable of reliably stopping attacks which leverage nontrivial injection contexts. To demonstrate this, we conduct a thorough analysis of the current state-of-the-art in browser-based XSS filtering and uncover a set of conceptual shortcomings, that allow efficient creation of filter evasions, especially in the case of DOM-based XSS. To validate our findings, we report on practical experiments using a set of 1,602 real-world vulnerabilities, achieving a rate of 73% successful filter bypasses. Motivated by our findings, we propose an alternative filter design for DOM-based XSS, that utilizes runtime taint tracking and taint-aware parsers to stop the parsing of attacker-controlled syntactic content. To examine the efficiency and feasibility of our approach, we present a practical implementation based on the open source browser Chromium. Our proposed approach has a low false positive rate and robustly protects against DOM-based XSS exploits.

The Web’s principal security policy is the Same-Origin Policy (SOP), which enforces origin-based isolation of mutually distrusting Web applications. Since the early days, the SOP was repeatedly undermined with variants of the DNS Rebinding attack, allowing untrusted script code to gain illegitimate access to protected network resources. To counter these attacks, the browser vendors introduced countermeasures, such as DNS Pinning, to mitigate the attack. In this paper, we present a novel DNS Rebinding attack method leveraging the HTML5 Application Cache. Our attack allows reliable DNS Rebinding attacks, circumventing all currently deployed browser-based defense measures. Furthermore, we analyze the fundamental problem which allows DNS Rebinding to work in the first place: The SOP’s main purpose is to ensure security boundaries of Web servers. However, the Web servers themselves are only indirectly involved in the corresponding security decision. Instead, the SOP relies on information obtained from the domain name system, which is not necessarily controlled by the Web server’s owners. This mismatch is exploited by DNS Rebinding. Based on this insight, we propose a light-weight extension to the SOP which takes Web server provided information into account. We successfully implemented our extended SOP for the Chromium Web browser and report on our implementation’s interoperability and security properties.

In recent years, the Web witnessed a move towards sophisticated client-side functionality. This shift caused a significant increase in complexity of deployed JavaScript code and thus, a proportional growth in potential client-side vulnerabilities, with DOM-based Cross-site Scripting being a high impact representative of such security issues. In this paper, we present a fully automated system to detect and validate DOM-based XSS vulnerabilities, consisting of a taint-aware JavaScript engine and corresponding DOM implementation as well as a context-sensitive exploit generation approach. Using these components, we conducted a large-scale analysis of the Alexa top 5000. In this study, we identified 6,167 unique vulnerabilities distributed over 480 domains, showing that 9,6% of the examined sites carry at least one DOM- based XSS problem.