| Total: 44
Recurring vulnerabilities widely exist and remain undetected in real-world systems, which are often resulted from reused code base or shared code logic. However, the potentially small differences between vulnerable functions and their patched functions as well as the possibly large differences between vulnerable functions and target functions to be detected bring challenges to clone-based and function matching-based approaches to identify these recurring vulnerabilities, i.e., causing high false positives and false negatives. In this paper, we propose a novel approach to detect recurring vulnerabilities with low false positives and low false negatives. We first use our novel program slicing to extract vulnerability and patch signatures from vulnerable function and its patched function at syntactic and semantic levels. Then a target function is identified as potentially vulnerable if it matches the vulnerability signature but does not match the patch signature. We implement our approach in a tool named MVP. Our evaluation on ten open-source systems has shown that, i) MVP significantly outperformed state-of-the-art clone-based and function matching-based recurring vulnerability detection approaches; ii) MVP detected recurring vulnerabilities that cannot be detected by general-purpose vulnerability detection approaches, i.e., two learning-based approaches and two commercial tools; and iii) MVP has detected 97 new vulnerabilities with 23 CVE identifiers assigned.
Ad-blocking applications have become increasingly popular among Internet users. Ad-blockers offer various privacy- and security-enhancing features: they can reduce personal data collection and exposure to malicious advertising, help safeguard users' decision-making autonomy, reduce users' costs (by increasing the speed of page loading), and improve the browsing experience (by reducing visual clutter). On the other hand, the online advertising industry has claimed that ads increase consumers' economic welfare by helping them find better, cheaper deals faster. If so, using ad-blockers would deprive consumers of these benefits. However, little is known about the actual economic impact of ad-blockers. We designed a lab experiment (N=212) with real economic incentives to understand the impact of ad-blockers on consumers' product searching and purchasing behavior, and the resulting consumer outcomes. We focus on the effects of blocking contextual ads (ads targeted to individual, potentially sensitive, contexts, such as search queries in a search engine or the content of web pages) on how participants searched for and purchased various products online, and the resulting consumer welfare. We find that blocking contextual ads did not have a statistically significant effect on the prices of products participants chose to purchase, the time they spent searching for them, or how satisfied they were with the chosen products, prices, and perceived quality. Hence we do not reject the null hypothesis that consumer behavior and outcomes stay constant when such ads are blocked or shown. We conclude that the use of ad-blockers does not seem to compromise consumer economic welfare (along the metrics captured in the experiment) in exchange for privacy and security benefits. We discuss the implications of this work in terms of end-users' privacy, the study's limitations, and future work to extend these results.
Despite an extensive anti-phishing ecosystem, phishing attacks continue to capitalize on gaps in detection to reach a significant volume of daily victims. In this paper, we isolate and identify these detection gaps by measuring the end-to-end life cycle of large-scale phishing attacks. We develop a unique framework—Golden Hour—that allows us to passively measure victim traffic to phishing pages while proactively protecting tens of thousands of accounts in the process. Over a one year period, our network monitor recorded 4.8 million victims who visited phishing pages, excluding crawler traffic. We use these events and related data sources to dissect phishing campaigns: from the time they first come online, to email distribution, to visitor traffic, to ecosystem detection, and finally to account compromise. We find the average campaign from start to the last victim takes just 21 hours. At least 7.42% of visitors supply their credentials and ultimately experience a compromise and subsequent fraudulent transaction. Furthermore, a small collection of highly successful campaigns are responsible for 89.13% of victims. Based on our findings, we outline potential opportunities to respond to these sophisticated attacks.
Smartphones and their applications have become a predominant way of computing, and it is only natural that they have become an important part of financial transaction technology. However, applications asking users to enter credit card numbers have been largely overlooked by prior studies, which frequently report pervasive security and privacy concerns in the general mobile application ecosystem. Such applications are particularly security-sensitive, and they are subject to the Payment Card Industry Data Security Standard (PCI DSS). In this paper, we design a tool called Cardpliance, which bridges the semantics of the graphical user interface with static program analysis to capture relevant requirements from PCI DSS. We use Cardpliance to study 358 popular applications from Google Play that ask the user to enter a credit card number. Overall, we found that 1.67% of the 358 applications are not compliant with PCI DSS, with vulnerabilities including improperly storing credit card numbers and card verification codes. These findings paint a largely positive picture of the state of PCI DSS compliance of popular Android applications.
Component-based software design is a primary engineering approach for building modern software systems. This programming paradigm, however, creates security concerns due to the potential for inconsistent interpretations of messages between different components. In this paper, we leverage such inconsistencies to identify vulnerabilities in email systems. We identify a range of techniques to induce inconsistencies among different components across email servers and clients. We show that these inconsistencies can enable attackers to bypass email authentication to impersonate arbitrary senders, and forge DKIM-signed emails with a legitimate site's signature. Using a combination of manual analysis and black-box fuzzing, we discovered 18 types of evasion exploits and tested them against 10 popular email providers and 19 email clients—all of which proved vulnerable to various attacks. Absent knowledge of our attacks, for many of them even a conscientious security professional using a state-of-the-art email provider service like Gmail cannot with confidence readily determine, when receiving an email, whether it is forged.
In a model extraction attack, an adversary steals a copy of a remotely deployed machine learning model, given oracle prediction access. We taxonomize model extraction attacks around two objectives: accuracy, i.e., performing well on the underlying learning task, and fidelity, i.e., matching the predictions of the remote victim classifier on any input. To extract a high-accuracy model, we develop a learning-based attack exploiting the victim to supervise the training of an extracted model. Through analytical and empirical arguments, we then explain the inherent limitations that prevent any learning-based strategy from extracting a truly high-fidelity model—i.e., extracting a functionally-equivalent model whose predictions are identical to those of the victim model on all possible inputs. Addressing these limitations, we expand on prior work to develop the first practical functionally-equivalent extraction attack for direct extraction (i.e., without training) of a model's weights. We perform experiments both on academic datasets and a state-of-the-art image classifier trained with 1 billion proprietary images. In addition to broadening the scope of model extraction research, our work demonstrates the practicality of model extraction attacks against production-grade systems.
Facebook usage is growing in developing countries, but we know little about how to tailor social media privacy settings to users in resourced-constrained settings. To that end, we present findings from interviews of 52 current mobile social media users in South Africa. We found users' primary privacy-related concern was who else could see their posts and messages, not what data the platforms or advertisers collect about them. Second, users displayed general knowledge gaps on existing social media privacy settings and relied heavily on blocking and passwords for privacy protection. Third, users' privacy and security-related behaviors were heavily influenced by living in high-crime areas. Based on these findings, we make recommendations for future work to better serve user privacy and security needs in resourced-constrained settings.
SpecFuzz is the first tool that enables dynamic testing for speculative execution vulnerabilities (e.g., Spectre). The key is a novel concept of speculation exposure: The program is instrumented to simulate speculative execution in software by forcefully executing the code paths that could be triggered due to mispredictions, thereby making the speculative memory accesses visible to integrity checkers (e.g., AddressSanitizer). Combined with the conventional fuzzing techniques, speculation exposure enables more precise identification of potential vulnerabilities compared to state-of-the-art static analyzers. Our prototype for detecting Spectre V1 vulnerabilities successfully identifies all known variations of Spectre V1 and decreases the mitigation overheads across the evaluated applications, reducing the amount of instrumented branches by up to 77% given a sufficient test coverage.
Modern society is increasingly surrounded by, and is growing accustomed to, a wide range of Cyber-Physical Systems (CPS), Internet-of-Things (IoT), and smart devices. They often perform safety-critical functions, e.g., personal medical devices, automotive CPS as well as industrial and residential automation, e.g., sensor-alarm combinations. On the lower end of the scale, these devices are small, cheap and specialized sensors and/or actuators. They tend to host small anemic CPUs, have small amounts of memory and run simple software. If such devices are left unprotected, consequences of forged sensor readings or ignored actuation commands can be catastrophic, particularly, in safety-critical settings. This prompts the following three questions: (1) How to trust data produced, or verify that commands were performed, by a simple remote embedded device?, (2) How to bind these actions/results to the execution of expected software? and, (3) Can (1) and (2) be attained even if all software on a device can be modified and/or compromised? In this paper we answer these questions by designing, demonstrating security of, and formally verifying, APEX: an Architecture for Provable Execution. To the best of our knowledge, this is the first of its kind result for low-end embedded systems. Our work has a range of applications, especially, authenticated sensing and trustworthy actuation, which are increasingly relevant in the context of safety-critical systems. APEX is publicly available and our evaluation shows that it incurs low overhead, affordable even for very low-end embedded devices, e.g., those based on TI MSP430 or AVR ATmega processors.
The security of FPGAs is a crucial topic, as any vulnerability within the hardware can have severe consequences, if they are used in a secure design. Since FPGA designs are encoded in a bitstream, securing the bitstream is of the utmost importance. Adversaries have many motivations to recover and manipulate the bitstream, including design cloning, IP theft, manipulation of the design, or design subversions e.g., through hardware Trojans. Given that FPGAs are often part of cyber-physical systems e.g., in aviation, medical, or industrial devices, this can even lead to physical harm. Consequently, vendors have introduced bitstream encryption, offering authenticity and confidentiality. Even though attacks against bitstream encryption have been proposed in the past, e.g., side-channel analysis and probing, these attacks require sophisticated equipment and considerable technical expertise. In this paper, we introduce novel low-cost attacks against the Xilinx 7-Series (and Virtex-6) bitstream encryption, resulting in the total loss of authenticity and confidentiality. We exploit a design flaw which piecewise leaks the decrypted bitstream. In the attack, the FPGA is used as a decryption oracle, while only access to a configuration interface is needed. The attack does not require any sophisticated tools and, depending on the target system, can potentially be launched remotely. In addition to the attacks, we discuss several countermeasures.
Increasingly mobile device users are being hurt by security or privacy issues with the apps they use. App developers can help prevent this; inexpensive security assurance techniques to do so are now well established, but do developers use them? And if they do so, is that reflected in more secure apps? From a survey of 335 successful app developers, we conclude that less than a quarter of such professionals have access to security experts; that less than a third use assurance techniques regularly; and that few have made more than cosmetic changes as a result of the European GDPR legislation. Reassuringly, we found that app developers tend to use more assurance techniques and make more frequent security updates when (1) they see more need for security, and (2) there is security expert or champion involvement. In a second phase we downloaded the apps corresponding to each completed survey and analyzed them for SSL issues, cryptographic API misuse and privacy leaks, finding only one fifth defect-free as far as our tools could detect. We found that having security experts or champions involved led to more cryptographic API issues, probably because of greater cryptography usage; but that measured defect counts did not relate to the need for security, nor to the use of assurance techniques. This offers two major opportunities for research: to further improve the detection of security issues in app binaries; and to support increasing the use of assurance techniques in the app developer community.
Disassembly is fundamental to binary analysis and rewriting. We present a novel disassembly technique that takes a stripped binary and produces reassembleable assembly code. The resulting assembly code has accurate symbolic information, providing cross-references for analysis and to enable adjustment of code and data pointers to accommodate rewriting. Our technique features multiple static analyses and heuristics in a combined Datalog implementation. We argue that Datalog’s inference process is particularly well suited for disassembly and the required analyses. Our implementation and experiments support this claim. We have implemented our approach into an open-source tool called Ddisasm. In extensive experiments in which we rewrite thousands of x64 binaries we find Ddisasm is both faster and more accurate than the current state-of-the-art binary reassembling tool, Ramblr.
Network covert channels are an advanced threat to the security of distributed systems. Existing defenses all come at the cost of performance, so they present significant barriers to a practical deployment in high-speed networks. We propose NetWarden, a novel defense whose key design goal is to preserve TCP performance while mitigating covert channels. The use of programmable data planes makes it possible for NetWarden to adapt defenses that were only demonstrated before as proof of concept, and apply them at linespeed. Moreover, NetWarden uses a set of performance boosting techniques to temporarily increase the performance of connections that have been affected by covert channel mitigation, with the ultimate goal of neutralizing the overall performance impact. NetWarden also uses a fastpath/slowpath architecture to combine the generality of software and the efficiency of hardware for effective defense. Our evaluation shows that NetWarden works smoothly with complex applications and workloads, and that it can mitigate covert timing and storage channels with little performance disturbance.
Caches have become the prime method for unintended information extraction across logical isolation boundaries. They are widely available on all major CPU platforms and, as a side channel, caches provide great resolution, making them the most convenient channel for Spectre and Meltdown. As a consequence, several methods to stop cache attacks by detecting them have been proposed. Detection is strongly aided by the fact that observing cache activity of co-resident processes is not possible without altering the cache state and thereby forcing evictions on the observed processes. In this work we show that this widely held assumption is incorrect. Through clever usage of the cache replacement policy, it is possible to track cache accesses of a victim's process without forcing evictions on the victim's data. Hence, online detection mechanisms that rely on these evictions can be circumvented as they would not detect the introduced RELOAD+REFRESH attack. The attack requires a profound understanding of the cache replacement policy. We present a methodology to recover the replacement policy and apply it to the last five generations of Intel processors. We further show empirically that the performance of RELOAD+REFRESH on cryptographic implementations is comparable to that of other widely used cache attacks, while detection methods that rely on L3 cache events are successfully thwarted.
One of the key questions when fuzzing is where to look for vulnerabilities. Coverage-guided fuzzers indiscriminately optimize for covering as much code as possible given that bug coverage often correlates with code coverage. Since code coverage overapproximates bug coverage, this approach is less than ideal and may lead to non-trivial time-to-exposure (TTE) of bugs. Directed fuzzers try to address this problem by directing the fuzzer to a basic block with a potential vulnerability. This approach can greatly reduce the TTE for a specific bug, but such special-purpose fuzzers can then greatly underapproximate overall bug coverage. In this paper, we present sanitizer-guided fuzzing, a new design point in this space that specifically optimizes for bug coverage. For this purpose, we make the key observation that while the instrumentation performed by existing software sanitizers are regularly used for detecting fuzzer-induced error conditions, they can further serve as a generic and effective mechanism to identify interesting basic blocks for guiding fuzzers. We present the design and implementation of ParmeSan, a new sanitizer-guided fuzzer that builds on this observation. We show that ParmeSan greatly reduces the TTE of real-world bugs, and finds bugs 37% faster than existing state-of-the-art coverage-based fuzzers (Angora) and 288% faster than directed fuzzers (AFLGo), while still covering the same set of bugs.
The introduction of smart contracts has significantly advanced the state-of-the-art in cryptocurrencies. Smart contracts are programs who live on the blockchain, governing the flow of money. However, the promise of monetary gain has attracted miscreants, resulting in spectacular hacks which resulted in the loss of millions worth of currency. In response, several powerful static analysis tools were developed to address these problems. We surveyed eight recently proposed static analyzers for Ethereum smart contracts and found that none of them captures all relevant features of the Ethereum ecosystem. For example, we discovered that a precise memory model is missing and inter-contract analysis is only partially supported. Based on these insights, we present the design and implementation of, a bounded model checker based on symbolic execution which provides a precise model of the Ethereum network. We demonstrate its capabilities in a series of experiments. First, we compare against the eight aforementioned tools, showing that even relatively simple toy examples can obstruct other analyzers. Further proving that precise modeling is indispensable, we leverage ETHBmc capabilities for automatic vulnerability scanning. We perform a large-scale analysis of roughly 2.2 million accounts currently active on the blockchain and automatically generate 5,905 valid inputs which trigger a vulnerability. From these, 1,989 can destroy a contract at will (so called suicidal contracts) and the rest can be used by an adversary to arbitrarily extract money. Finally, we compare our large-scale analysis against two previous analysis runs, finding significantly more inputs (22.8%) than previous approaches.
Fuzzing is a testing technique to discover unknown vulnerabilities in software. When applying fuzzing to libraries, the core idea of supplying random input remains unchanged, yet it is non-trivial to achieve good code coverage. Libraries cannot run as standalone programs, but instead are invoked through another application. Triggering code deep in a library remains challenging as specific sequences of API calls are required to build up the necessary state. Libraries are diverse and have unique interfaces that require unique fuzzers, so far written by a human analyst. To address this issue, we present FuzzGen, a tool for automatically synthesizing fuzzers for complex libraries in a given environment. FuzzGen leverages a whole system analysis to infer the library’s interface and synthesizes fuzzers specifically for that library. FuzzGen requires no human interaction and can be applied to a wide range of libraries. Furthermore, the generated fuzzers leverage LibFuzzer to achieve better code coverage and expose bugs that reside deep in the library. FuzzGen was evaluated on Debian and the Android Open Source Project (AOSP) selecting 7 libraries to generate fuzzers. So far, we have found 17 previously unpatched vulnerabilities with 6 assigned CVEs. The generated fuzzers achieve an average of 54.94% code coverage; an improvement of 6.94% when compared to manually written fuzzers, demonstrating the effectiveness and generality of FuzzGen.
The k-Nearest Neighbor Search (k-NNS) is the backbone of several cloud-based services such as recommender systems, face recognition, and database search on text and images. In these services, the client sends the query to the cloud server and receives the response in which case the query and response are revealed to the service provider. Such data disclosures are unacceptable in several scenarios due to the sensitivity of data and/or privacy laws. In this paper, we introduce SANNS, a system for secure k-NNS that keeps client's query and the search result confidential. SANNS comprises two protocols: an optimized linear scan and a protocol based on a novel sublinear time clustering-based algorithm. We prove the security of both protocols in the standard semi-honest model. The protocols are built upon several state-of-the-art cryptographic primitives such as lattice-based additively homomorphic encryption, distributed oblivious RAM, and garbled circuits. We provide several contributions to each of these primitives which are applicable to other secure computing tasks. Both of our protocols rely on a new circuit for the approximate top-k selection from n numbers that is built from O(n + k2) comparators. We have implemented our proposed system and performed extensive experimental results on four datasets in two different computation environments, demonstrating more than 18 — 31 × faster response time compared to optimally implemented protocols from the prior work. Moreover, SANNS is the first work that scales to the database of 10 million entries, pushing the limit by more than two orders of magnitude.
Error handling code is often critical but difficult to test in reality. As a result, many hard-to-find bugs exist in error handling code and may cause serious security problems once triggered. Fuzzing has become a widely used technique for finding software bugs nowadays. Fuzzing approaches mutate and/or generate various inputs to cover infrequently-executed code. However, existing fuzzing approaches are very limited in testing error handling code, because some of this code can be only triggered by occasional errors (such as insufficient memory and network-connection failures), but not specific inputs. Therefore, existing fuzzing approaches in general cannot effectively test such error handling code. In this paper, we propose a new fuzzing framework named FIFUZZ, to effectively test error handling code and detect bugs. The core of FIFUZZ is a context-sensitive software fault injection (SFI) approach, which can effectively cover error handling code in different calling contexts to find deep bugs hidden in error handling code with complicated contexts. We have implemented FIFUZZ and evaluated it on 9 widely-used C programs. It reports 317 alerts which are caused by 50 unique bugs in terms of the root causes. 32 of these bugs have been confirmed by related developers. We also compare FIFUZZ to existing fuzzing tools (including AFL, AFLFast, AFLSmart and FairFuzz), and find that FIFUZZ finds many bugs missed by these tools. We believe that FIFUZZ can effectively augment existing fuzzing approaches to find many real bugs that have been otherwise missed.
Android devices ship with pre-installed privileged apps in their firmware — some of which are essential system components, others deliver a unique user experience — that users cannot disable. These pre-installed apps are assumed to be secure as they are handpicked or developed by the device vendors themselves rather than third parties. Unfortunately, we have identified an alarming number of Android firmware that contain privilege-escalation vulnerabilities in pre-installed apps, allowing attackers to perform unauthorized actions such as executing arbitrary commands, recording the device audio and screen, and accessing personal data to name a few. To uncover these vulnerabilities, we built FIRMSCOPE, a novel static analysis system that analyzes Android firmware to expose unwanted functionality in pre-installed apps using an efficient and practical context-sensitive, flow-sensitive, field-sensitive, and partially object-sensitive taint analysis. Our experimental results demonstrate that FIRMSCOPE significantly outperforms the state-of-the-art Android taint analysis solutions both in terms of detection power and runtime performance. We used FIRMSCOPE to scan 331,342 pre-installed apps in 2,017 Android firmware images from v4.0 to v9.0 from more than 100 Android vendors. Among them, FIRMSCOPE uncovered 850 unique privilege-escalation vulnerabilities, many of which are exploitable and 0-day.
Fuzzing is one of the most effective approaches for identifying security vulnerabilities. As a state-of-the-art coverage-based greybox fuzzer, AFL is a highly effective and widely used technique. However, AFL allocates excessive energy (i.e., the number of test cases generated by the seed) to seeds that exercise the high-frequency paths and can not adaptively adjust the energy allocation, thus wasting a significant amount of energy. Moreover, the current Markov model for modeling coverage-based greybox fuzzing is not profound enough. This paper presents a variant of the Adversarial Multi-Armed Bandit model for modeling AFL’s power schedule process. We first explain the challenges in AFL's scheduling algorithm by using the reward probability that generates a test case for discovering a new path. Moreover, we illustrated the three states of the seeds set and developed a unique adaptive scheduling algorithm as well as a probability-based search strategy. These approaches are implemented on top of AFL in an adaptive energy-saving greybox fuzzer called EcoFuzz. EcoFuzz is examined against other six AFL-type tools on 14 real-world subjects over 490 CPU days. According to the results, EcoFuzz could attain 214% of the path coverage of AFL with reducing 32% test cases generation of that of AFL. Besides, EcoFuzz identified 12 vulnerabilities in GNU Binutils and other software. We also extended EcoFuzz to test some IoT devices and found a new vulnerability in the SNMP component.
Intra-process memory isolation can improve security by enforcing least-privilege at a finer granularity than traditional operating system controls without the context-switch overhead associated with inter-process communication. A single process can be divided into separate components such that memory belonging to one component can only be accessed by the code of that component. Because the process has traditionally been a fundamental security boundary, assigning different levels of trust to components within a process is a fundamental change in secure systems design. However, so far there has been little research on the challenges of securely implementing intra-process isolation on top of existing operating system abstractions. We identify that despite providing strong intra-process memory isolation, existing, general purpose approaches neglect the ways in which the OS makes memory and other intra-process resources accessible through system objects. Using two recently-proposed memory isolation systems, we show that such designs are vulnerable to generic attacks that bypass memory isolation These attacks use the kernel as a confused deputy, taking advantage of existing intended kernel functionality that is agnostic of intra-process isolation. We argue that the root cause stems from a fundamentally different security model between kernel abstractions and user-level, intra-process memory isolation. Finally, we discuss potential mitigations and show that the performance cost of extending a ptrace-based sandbox to prevent the new attacks is high, highlighting the need for more efficient system call interception.
Exploitation techniques to abuse metadata of heap allocators have been widely studied because of their generality (i.e., application independence) and powerfulness (i.e., bypassing modern mitigation). However, such techniques are commonly considered arts, and thus the ways to discover them remain ad-hoc, manual, and allocator-specific. In this paper, we present an automatic tool, ArcHeap, to systematically discover the unexplored heap exploitation primitives, regardless of their underlying implementations. The key idea of ArcHeap is to let the computer autonomously explore the spaces, similar in concept to fuzzing, by specifying a set of common designs of modern heap allocators and root causes of vulnerabilities as models, and by providing heap operations and attack capabilities as actions. During the exploration, ArcHeap checks whether the combinations of these actions can be potentially used to construct exploitation primitives, such as arbitrary write or overlapped chunks. As a proof, ArcHeap generates working PoC that demonstrates the discovered exploitation technique. We evaluated ArcHeap with ptmalloc2 and 10 other allocators, and discovered five previously unknown exploitation techniques in ptmalloc2 as well as several techniques against seven out of 10 allocators including the security-focused allocator, DieHarder. To show the effectiveness of ArcHeap's approach in other domains, we also studied how security features and exploit primitives evolve across different versions of ptmalloc2.
Fingerprint authentication has gained increasing popularity on mobile devices in recent years. However, it is vulnerable to presentation attacks, which include that an attacker spoofs with an artificial replica. Many liveness detection solutions have been proposed to defeat such presentation attacks; however, they all fail to defend against a particular type of presentation attack, namely puppet attack, in which an attacker places an unwilling victim’s finger on the fingerprint sensor. In this paper, we propose FINAUTH, an effective and efficient software-only solution, to complement fingerprint authentication by defeating both synthetic spoofs and puppet attacks using fingertip-touch characteristics. FINAUTH characterizes intrinsic fingertip-touch behaviors including the acceleration and the rotation angle of mobile devices when a legitimate user authenticates. FINAUTH only utilizes common sensors equipped on mobile devices and does not introduce extra usability burdens on users. To evaluate the effectiveness of FINAUTH, we carried out experiments on datasets collected from 90 subjects after the IRB approval. The results show that FINAUTH can achieve the average balanced accuracy of 96.04% with 5 training data points and 99.28% with 100 training data points. Security experiments also demonstrate that FINAUTH is resilient against possible attacks. In addition, we report the usability analysis results of FINAUTH, including user authentication delay and overhead.
Click-jacking protection on the modern Web is commonly enforced via client-side security mechanisms for framing control, like the X-Frame-Options header (XFO) and Content Security Policy (CSP). Though these client-side security mechanisms are certainly useful and successful, delegating protection to web browsers opens room for inconsistencies in the security guarantees offered to users of different browsers. In particular, inconsistencies might arise due to the lack of support for CSP and the different implementations of the underspecified XFO header. In this paper, we formally study the problem of inconsistencies in framing control policies across different browsers and we implement an automated policy analyzer based on our theory, which we use to assess the state of click-jacking protection on the Web. Our analysis shows that 10% of the (distinct) framing control policies in the wild are inconsistent and most often do not provide any level of protection to at least one browser. We thus propose recommendations for web developers and browser vendors to mitigate this issue. Finally, we design and implement a server-side proxy to retrofit security in web applications.