Last-Mile TLS Interception: Analysis and Observation of the Non-Public HTTPS Ecosystem

Transport Layer Security (TLS) is one of the most widely deployed cryptographic protocols on the Internet that provides confidentiality, integrity, and a certain degree of authenticity of the communications between clients and servers. Following Snowden's revelations on US surveillance programs, the adoption of TLS has steadily increased. However, encrypted traffic prevents legitimate inspection. Therefore, security solutions such as personal antiviruses and enterprise firewalls may intercept encrypted connections in search for malicious or unauthorized content. Therefore, the end-to-end property of TLS is broken by these TLS proxies (a.k.a. middleboxes) for arguably laudable reasons; yet, may pose a security risk. While TLS clients and servers have been analyzed to some extent, such proxies have remained unexplored until recently. We propose a framework for analyzing client-end TLS proxies, and apply it to 14 consumer antivirus and parental control applications as they break end-to-end TLS connections. Overall, the security of TLS connections was systematically worsened compared to the guarantees provided by modern browsers. Next, we aim at exploring the non-public HTTPS ecosystem, composed of locally-trusted proxy-issued certificates, from the user's perspective and from several countries in residential and enterprise settings. We focus our analysis on the long tail of interception events. We characterize the customers of network appliances, ranging from small/medium businesses and institutes to hospitals, hotels, resorts, insurance companies, and government agencies. We also discover regional cases of traffic interception malware/adware that mostly rely on the same Software Development Kit (i.e., NetFilter). Our scanning and analysis techniques allow us to identify more middleboxes and intercepting apps than previously found from privileged server vantages looking at billions of connections. We further perform a longitudinal study over six years of the evolution of a prominent traffic-intercepting adware found in our dataset: Wajam. We expose the TLS interception techniques it has used and the weaknesses it has introduced on hundreds of millions of user devices. This study also (re)opens the neglected problem of privacy-invasive adware, by showing how adware evolves sometimes stronger than even advanced malware and poses significant detection and reverse-engineering challenges. Overall, whether beneficial or not, TLS interception often has detrimental impacts on security without the end-user being alerted

From Very Weak to Very Strong: Analyzing Password-Strength Meters

Millions of users are exposed to password-strength meters/checkers at highly popular web services that use user- chosen passwords for authentication. Recent studies have found evidence that some meters actually guide users to choose better passwords—which is a fairly rare-bit of good news in password research. However, these meters are mostly based on ad-hoc design. At least, as we found, most vendors do not provide any explanation of their design choices, sometimes making them appear to be a black box. We analyze password meters deployed in selected popular websites, by measuring the strength labels assigned to common passwords from several password dictionaries. From this empirical analysis with millions of passwords, we report prominent characteristics of meters as deployed at popular websites. We shed light on how the server-end of some meters functions, provide examples of highly inconsistent strength outcomes for the same password in different meters, along with examples of many weak passwords being labeled as strong or even very strong. These weaknesses and inconsistencies may confuse users in choosing a stronger password, and thus may weaken the purpose of these meters. On the other hand, we believe these findings may help improve existing meters, and possibly make them an effective tool in the long run

A Large-Scale Evaluation of High-Impact Password Strength Meters

Passwords are ubiquitous in our daily digital life. They protect various types of assets ranging from a simple account on an online newspaper website to our health information on government websites. However, due to the inherent value they protect, malicious people have developed insights into cracking them. Users are pushed to choose stronger passwords to comply with password policies, which they may not like much. Another solution is to put in place proactive password-strength meters/checkers to give feedbacks to users while they create new passwords. Millions of users are now exposed to these meters at highly popular web services that use user-chosen passwords for authentication, or more recently in password managers. Recent studies have found evidence that some meters actually guide users to choose better passwords -which is a rare bit of good news in password research. However, these meters are mostly based on ad-hoc design. At least, as we found, most vendors do not provide any explanation of their design choices, sometimes making them appear as a black-box. We analyze password meters deployed in selected popular websites and password managers. We document obfuscated open-source meters; infer the algorithm behind the closed-source ones; and measure the strength labels assigned to common passwords from several password dictionaries. From this empirical analysis with millions of passwords, we shed light on how the server-end of some web service meters functions, provide examples of highly inconsistent strength outcomes for the same password in different meters, along with examples of many weak passwords being labeled as strong or even excellent. These weaknesses and inconsistencies may confuse users in choosing a stronger password, and thus may weaken the purpose of these meters. On the other hand, we believe these findings may help improve existing meters, and possibly make them an effective tool in the long run

Challenges and Implications of Verifiable Builds for Security-Critical Open-Source Software

The majority of computer users download software from the Internet and run it directly on their machine. They expect applications to work as advertised, and implicitly trust them not to perform any malicious activities. For security-sensitive applications though, users need the assurance that what they downloaded is what has been officially released by the developers, and that it comes directly from audited sources to avoid surreptitious backdoors. However, the compilation process from source code to binary files, and more generally, the toolchain used in software packaging, has not been designed with verifiability in mind. Rather, the output of compilers is often dependent on parameters that can be strongly tied to the building environment, and may not be easily repeatable anywhere else. In this paper, we first manually replicate a close match to the official binaries of sixteen most recent versions of TrueCrypt for Windows up to v7.1a, a widely known open-source encryption tool, and explain the remaining differences that can solely be attributed to non-determinism in the build process. This experiment provides the missing guarantee on the official binaries, and makes audits on TrueCrypt's source code more meaningful. Also, it gives insights about what constitutes sources of non-determinism in a compilation process, which may help create future verifiable build processes. We also summarize challenges faced by Bitcoin, Tor, Debian and other Linux distributions in designing automated methods, such as deterministic and reproducible builds, for the verification of their official packages. Finally, we discuss a few suggestions for achieving deterministic builds

Challenges and Implications of Verifiable Builds for Security-Critical Open-Source Software

The majority of computer users download compiled software and run it directly on their machine. Apparently, this is also true for open-sourced software -- most users would not compile the available source, and implicitly trust that the available binaries have been compiled from the published source code (i.e., no backdoor has been inserted in the binary). To verify that the official binaries indeed correspond to the released source, one can compile the source of a given application, and then compare the locally generated binaries with the developer-provided official ones. However, such simple verification is non-trivial to achieve in practice, as modern compilers, and more generally, toolchains used in software packaging, have not been designed with verifiability in mind. Rather, the output of compilers is often dependent on parameters that can be strongly tied to the building environment. In this paper, we analyze a widely-used encryption tool, TrueCrypt, to verify its official binary with the corresponding source. We first manually replicate a close match to the official binaries of sixteen most recent versions of TrueCrypt for Windows up to v7.1a, and then explain the remaining differences that can solely be attributed to non-determinism in the build process. Our analysis provides the missing guarantee on official binaries that they are indeed backdoor-free, and makes audits on TrueCrypt's source code more meaningful. Also, we uncover several sources of non-determinism in TrueCrypt's compilation process; these findings may help create future verifiable build processes

From Very Weak to Very Strong: Analyzing Password-Strength Meters

Millions of users are exposed to password-strength meters/checkers at highly popular web services that use user- chosen passwords for authentication. Recent studies have found evidence that some meters actually guide users to choose better passwords—which is a fairly rare-bit of good news in password research. However, these meters are mostly based on ad-hoc design. At least, as we found, most vendors do not provide any explanation of their design choices, sometimes making them appear to be a black box. We analyze password meters deployed in selected popular websites, by measuring the strength labels assigned to common passwords from several password dictionaries. From this empirical analysis with millions of passwords, we report prominent characteristics of meters as deployed at popular websites. We shed light on how the server-end of some meters functions, provide examples of highly inconsistent strength outcomes for the same password in different meters, along with examples of many weak passwords being labeled as strong or even very strong. These weaknesses and inconsistencies may confuse users in choosing a stronger password, and thus may weaken the purpose of these meters. On the other hand, we believe these findings may help improve existing meters, and possibly make them an effective tool in the long run

PSV (Password Security Visualizer): From Password Checking to User Education

This paper presents the Password Security Visualizer (PSV), an interactive visualization system specifically designed for password security education. PSV can be seen as a reconfigurable "box" containing different proactive password checkers (PPCs) and visualizers of password security information, allowing it to be used like a "many in one" or "hybrid" PPC. PSV can provide many new features that do not exist in traditional PPCs, thus having a greater potential to achieve its goals of educating users. Using purely client-side Web-based technologies, we implemented a prototype of PSV as an open-source software tool on a 2-D animated canvas. To evaluate the actual performance of our implemented PSV prototype against traditional PPCs, we conducted a semi-structured interview involving 20 human participants. Our qualitative analysis of the results showed that PSV was considered the most informative and recommended by most participants as a good educational tool. To the best of our knowledge, PSV is the first system combining different PPCs together for user education, and the user study is the first of this kind on comparing educational effectiveness of different PPCs (and PPC-like password security tools such as PSV)

Enhancing security behaviour by supporting the user

Although the role of users in maintaining security is regularly emphasized, this is often not matched by an accompanying level of support. Indeed, users are frequently given insufficient guidance to enable effective security choices and decisions, which can lead to perceived bad behaviour as a consequence. This paper discusses the forms of support that are possible, and seeks to investigate the effect of doing so in practice. Specifically, it presents findings from two experimental studies that investigate how variations in password meter usage and feedback can positively affect the resulting password choices. The first experiment examines the difference between passwords selected by unguided users versus those receiving guidance and alternative forms of feedback (ranging from a traditional password meter through to an emoji-based approach). The findings reveal a 30% drop in weak password choices between unguided and guided usage, with the varying meters then delivering up to 10% further improvement. The second experiment then considers variations in the form of feedback message that users may receive in addition to a meter-based rating. It is shown that by providing richer information (e.g. based upon the time required to crack a password, its relative ranking against other choices, or the probability of it being cracked), users are more motivated towards making strong choices and changing initially weak ones. While the specifics of the experimental findings were focused upon passwords, the discussion also considers the benefits that may be gained by applying the same principles of nudging and guidance to other areas of security in which users are often found to have weak behaviours