Shepherd: Enabling Automatic and Large-Scale Login Security Studies

08/02/2018
by   Hugo Jonker, et al.
TH Köln
Open Universiteit
0

More and more parts of the internet are hidden behind a login field. This poses a barrier to any study predicated on scanning the internet. Moreover, the authentication process itself may be a weak point. To study authentication weaknesses at scale, automated login capabilities are needed. In this work we introduce Shepherd, a scanning framework to automatically log in on websites. The Shepherd framework enables us to perform large-scale scans of post-login aspects of websites. Shepherd scans a website for login fields, attempts to submit credentials and evaluates whether login was successful. We illustrate Shepherd's capabilities by means of a scan for session hijacking susceptibility. In this study, we use a set of unverified website credentials, some of which will be invalid. Using this set, Shepherd is able to fully automatically log in and verify that it is indeed logged in on 6,273 unknown sites, or 12.4 2,579 sites, i.e., 41.4

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1 Introduction

As online services play an ever-increasing role in daily life, the ability to ascertain privacy and security of these services grows evermore important. Recent studies into privacy and security of websites either scan up to the login point, or use manual logins. However, more and more content is hidden behind logins. This causes two problems. First and foremost, it hinders content shielded by a login from being evaluated by such studies. Recent frameworks such as FPDetective [AJN13] or OpenWPM [EN16] focus on studying privacy aspects of websites. Such frameworks can be – and have been – used to study how websites handle various privacy aspects of their visitors. However, to study the privacy of website users, more is needed: a scanner has to look beyond the login point. While this is relatively easily done manually, or with user assistance, neither of those approaches scale well. A second problem follows from the fact that an authentication process is a security process – and it may be insecure. Indeed, the Open Web Application Security Project (OWASP) has consistently ranked “broken authentication and session management” in the top 3 security problems for websites since 2010 [OWA17]

. As the number of sites with logins increase, the amount of sites with problems in this category is likely to increase as well. Estimating how many sites are insecure cannot be done manually – there are simply too many websites. Some form of automation is needed, yet there currently are no tools to facilitate such studies. This is acknowledged as a difficult problem. For example, Tang, Dautenhahn and King state that “

it is infeasible to evaluate cookie protection automatically” [TDK11]. Indeed, Qualys SSL Lab’s SSL server test may award top marks to a website that is susceptible to simple session hijacking, which is clearly stipulated by Qualys.

There are two approaches to circumventing this problem: using manual intervention or piggy-backing on single sign-on frameworks. However, each of these has its limitations: manual intervention does not scale well, and logging in with single sign-on frameworks often requires a ‘registration’ step on the site in question, and is therefore not akin to an actual login.

We present Shepherd, a scanning framework that is able to guide (or “shepherd”) scans beyond a login barrier. Firstly, Shepherd enables a sorely needed automated, large-scale evaluation of authentication process security. But secondly, and more importantly, it also enables any other scan to be conducted on those parts of the internet for which a login is required.

1.0.1 Contributions.

This work presents the following contributions:

  • We design and develop Shepherd, a tool that can perform large-scale scans of websites that require logins. This opens up the possibility to perform security scans on websites beyond the login barrier.

  • We illustrate Shepherd’s capabilities by studying session hijacking susceptibility, with two sources for login credentials: BugMeNot, an open (but biased) database of usernames and passwords, and Facebook Single Sign On.

    • We show an approach to probe a user’s cookie jar for session cookies of targeted sites.

    • Using Shepherd, we can scan an order of magnitude more sites than achieved in previous (typically manually-assisted) studies.

    • We find that out of 6,273 sites (from BugMeNot) scanned for session hijacking susceptibility, at least 2,579 sites (i.e., 41.4%) are vulnerable.

    • BugMeNot is a biased source of credentials (certain websites are excluded, and any website is removed upon request). We compare the results of the biased BugMeNot experiment with the results of the (smaller) Facebook experiment.

1.0.2 Ethical considerations.

Executing this study leads to several ethical concerns. First of all, we submitted our study to our Institutional Review Board for Computer Science and incorporated their advice in our studies. Secondly, we faced the question of how to acquire a large set of login credentials from a legitimate source. Fortunately, the BugMeNot database is exactly this: a large set of login credentials with strict (and enforced) policies to ban a site from inclusion upon request of the site owner. Thirdly, the experiments must not exceed their mandate and break things. To this end, the experiments were confined to only execute a login. The security evaluation then occurred on the client side. Thus, the connection was broken following the response to a login attempt (irrespective of the success of the attempt). We worked on this by testing Shepherd on a small number of domains and resolving any issues. The results are not 100% perfect, but the fraction of mistakingly pressed buttons we detected is very small. Fourthly, the results could be used to create attacks against specific websites. Therefore, we only discuss results in aggregate, not for particular sites. Fourthly, the tools created can easily be misused. Therefore, we will not publicly release them, but we will make them available to other bona fide researchers and reviewers upon request. Finally, we used responsible disclosure to notify sites we manually found to be vulnerable.

2 Related work

Several studies have been performed that needed to authenticate with websites. Typically, these studies leveraged manual intervention to achieve logins. While this approach successfully accounts for the post-login stage, the studies using this approach are limited in scale to the participants and difficult to repeat.

Mundada et al. [MFK16] let participants log into a websites to analyse security of the login process of 149 sites. They found several security risks in well-known sites such as Yahoo. These findings underscore the importance of extending such studies to more sites. However, Mundada et al. asked volunteers to log in, which is labor-intensive.

Similarly, Wang et al. [WCW12] study the security of various single sign-on implementations by manually logging in. They uncovered various security flaws in widely-used single sign-on implementations. This is especially troubling given that since Wang et al. have performed their study, single sign-on services have been upgraded and updated, and typically are somewhat backwards compatible. Moreover, their study focuses on single sign-on service providers popular in the US, but does not account for single sign-on service providers from elsewhere111E.g., the service by Vkontakte, a Facebook alternative popular in Russia..

A different approach to logging in was taken by Kranch and Bonneau [KB15]. They studied the use of HSTS222Hypertext Strict Transport Security, telling the browser to only connect on future visits using HTTPS. and HKPK333HTTP Public Key Pinning, telling browsers to keep the server’s public key for future visits., scanning sites while being logged in with multiple single sign-on providers. They achieved this by keeping session cookies in the cookie jar. While this approach aims to study impacts of single sign-on providers on websites, it does not enable access to post-login areas.

To our best knowledge Robinson and Bonneau [RB14] provide the only work which attempts to automatically login into websites via single sign-on providers. However, the authors manually selected websites with Facebook Connect. Moreover, their study focused on what access to the user’s Facebook profile these sites obtain after logging in. Conversely, our work focuses not on the single sign-on provider, but on the logged-in site.

3 High-level overview of Shepherd

In this section, we present a high-level overview of the design of Shepherd, and discuss various challenges and design decisions. The high-level structure of Shepherd is depicted in Fig. 1. Shepherd consists of three phases:

  1. [I.]

  2. targeting phase, selecting targets of the scan and obtaining credentials;

  3. login phase, which performs the logins and evaluates their success;

  4. scanning phase, which executes the desired scan.

The targeting phase may be executed in a stand-alone fashion, or on the fly. In early testing, we found that on-the-fly targeting introduces bottlenecks, which are irrelevant to the actual scan. As such, we chose to redesign this phase to operate stand-alone, independent of the rest of the framework. As such, the results of the targeting phase are stored in a database, which can be read at any time by the subsequent phases.

Conversely, the actual evaluation of the scan may be executed on the fly, or a posteriori, by examining the data collected on the fly. In either case, some data needs to be processed, so at least part of the scanning phase (data collection) must operate on the fly.

Figure 1: Overview of Shepherd’s design (scanning phase shows the design of the experiment from Section 6.

The login phase is by far the most complex part of Shepherd, and merits separate discussion. This phase is discussed in detail in Sec. 4. Noteworthy details pertaining to the other phases are discussed below.

3.1 Preparation phase

Shepherd uses a list of target websites and corresponding credentials as input in order to login. The purpose of the preparation phase is to automatically obtain the credentials given the list of target websites. There are two options to obtain credentials automatically: registering new users (and thereby creating new credentials), or relying on existing databases of credentials.

Each of these approaches has drawbacks. A major drawback to automatically creating accounts is that typical account creation processes guard against precisely such actions (e.g. by means of captcha’s). This could be circumvented by manual creation, which is laborious and does not scale well. Conversely, while obtaining credentials from an existing database of credentials is more straightforward, this too has its downsides. A major methodological issue is that the selection of targeted websites is limited by the pre-existing database. A more practical consideration concerns the legitimacy of the database. We found various online services containing less-than-legally obtained credentials, e.g. passwords obtained from data breaches.

Shepherd’s targeting phase outputs credentials in a database. This allows alternative targeting approaches (e.g. manually-supplied credentials) in addition to automatically sourced credentials. For Shepherd’s current targeting phase, we source credentials from the BugMeNot website. BugMeNot provides logins to websites for users who do not wish “to be bugged”. To the best of our understanding, they provide a legitimate service, with restrictions on sites added: no pay-per-view sites, no banks, no age-restricted sites, and no sites whose content is fully accessible without login. While this allows us a rich source of credentials, it introduces also two issues. Firstly, the validity of the credentials is uncertain. Not only can credentials be invalid, BugMeNot can even have credentials for sites that do not have logins. Secondly, it introduces a bias in the sites studied. As mentioned, BugMeNot excludes several categories of sites, such as banks. This has to be taken into account when examining the results of any study.

3.2 Scanning phase

After logging in, the scan of the target website is executed. Shepherd is written in Python, therefore, scans may be expressed as modules in Python. Shepherd provides an interface to interact with the browser and detect effects of interactions. This interface is a wrapper of Selenium commands, but streamlines error handling and ensures performance-optimised commands are used by the scanning module. Optionally, multiple modules can be hooked into Shepherd. This allows for sequential execution of several scanning modules. Scan results are determined on the fly and stored in CSV files for a posteriori evaluation.

By default, Shepherd determines which cookies are authentication cookies and performs a security scan. However, it is possible to add custom interactions with the browser. Shepherd provides an interface which functions as a wrapper for Selenium commands. The results of the scan are stored in CSV files, which can be externally processed for a posteriori evaluation.

3.3 Implementation

Shepherd uses Selenium, a testing tool for automating interaction with browsers. According to Englehardt and Narayanan [EN16], headless browsers are sometimes served different content than full browsers. Moreover, they also found that in headless browsers, a substantial number of sites render incorrectly or crash due to missing plugins. To avoid these drawbacks, Shepherd uses a full browser for scanning. Shepherd can use either Chrome or Firefox as a designated browser, and runs on Linux and on MacOS systems.

3.3.1 Performance.

Logins are typically slow and can easily take several seconds. When attempting to login on unknown sites, using a form which may or may not be the login form, with credentials that may or may not be valid, several passes have to be taken. To study sites at scale with all these factors in mind thus necessitates high performance.

With respect to optimisation, we found that sometimes it pays to replace Selenium built-in functions with our own JavaScript code. Accessing the plain HTML content of elements takes 14 ms with Selenium. This results in high runtime, if a site uses a large number of elements. In one example, accessing and filtering elements took 16.8 sec compared to 50 msec with Javascript. Another example is Selenium’s function to query for multiple elements, which takes 1 sec per query. This sums quickly up, when executed multiple times, due to multiple windows and iframes. With Javascript the same operation plus applying filters to all elements can be completed in 200 msec.

3.3.2 Stability.

Shepherd’s stability is of course important when performing automated scans. There are several caveats that we encountered. First, some websites overwrite basic Javascript functions. This may cause problems, which is why Shepherd executes its Javascript in a separate window.

On the level of running processes, we found that sometimes, a browser instance crashes or runs out of memory. To mitigate out of memory errors, Shepherd executes browser instances as threads. If the instance exceeds memory limits, Shepherd kills the thread and starts a new instance. To mitigate crashes, browser instances are restarted after a number of runs. With these measures in place, we find Shepherd can scan and login to about 1500 sites per browser instance per day.

3.3.3 Single sign-on logins.

We experienced single sign-on logins as less effective than hoped. Typically, following authentication via single sign-on, the website requests that the user fills in a registration form of some kind. Access to other parts of the site is typically blocked until this form is filled in. This behaviour does not always occur, but frequently enough that it affects the study. This implies that single sign-on logins by themselves are insufficient to study the post-login aspects of websites. Nevertheless, single sign-on logins are effective in studying security of the login process itself.

To that end, we extended Shepherd with limited capabilities for using Facebook’s single sign-on service. This capability begins with searching for a Facebook login entry point on the website, which need not be present (in contrast to [RB14]). Once Shepherd believes it has found such an entry point, it processes the login as a regular login, but with the loaded Facebook credentials.

4 Login Phase Design

The design of the login phase is to consider the target website as a black box. That is: it cannot incorporate any knowledge about the site design or layout except that which it learns by scanning the site. Therefore, we device a generic model of the website login process. This model provides a three-step approach to logging in:

  1. Login page detection: in the first step, the login page of the site is identified. This step is concluded successfully once the login area is identified.

  2. Authentication: in the second step, credentials are submitted. This step is concluded successfully if a change in the website is detected following submission of credentials.

  3. Verification: the last step verifies whether the website is indeed logged in. Whereas the authentication step uses a crude measure (change in webpage), this step uses an array of more sophisticated techniques.

Note that for a fully automated approach, false positives and false negatives may crop up in each of these steps. We evaluate the performance of Shepherd in this respect in Section 6.2.

Each of these steps consists of numerous actions, leading to the model depicted in Figure 2. In this figure, the red boxes indicate successful conclusion of the corresponding step.

Figure 2: Overview of the login process.

4.1 Searching for login elements

As shown in Figure 2, different methods for finding login elements may be used (a). Shepherd uses the following five methods to find a login page. Starting from the landing page, Shepherd first looks for suitable login elements (method 1). Failing that, Shepherd selects elements with relations to login-based terms from the landing page. Selected urls are visited first (method 2). All links on the visited pages are cached for method 5. Should this step also fail, clickable elements are clicked (method 3). Failing this, Shepherd attempts a brute force search of the login page by appending login related keywords444Shepherd contains a dictionary with multiple translations for keywords from native speakers and Google translations. to the base url555Specifically: http://target/login, http://target/account and http://target/signin. (method 4). In case this also fails, the last approach (method 5) is to follow all links collected during method 2. This approach accounts for sites that require a user to follow more than one link before being able to enter a username, such as imdb.com.

After each action in finding login elements, Shepherd scans all open windows and iframes looking for a password field (cf. ‘b’ in Figure 2). When Shepherd encounters a visible input element of type password which is not part of a registration form, it assigns the status login page found. A form is considered a registration form if it contains more than 3 visible input elements (including the found password field).

If a candidate login page is found, Shepherd moves on to submitting credentials. Two types of credential submission processes are considered: one-step logins, in which a username and a password are submitted in one go, and two-step logins, in which the user needs to press a “submit” button after entering the username. One-step login candidates are easily detected by password elements. Conversely, two-step login elements must be distinguished from other input elements (e.g. search fields or newsletter sign-up forms), and can only be detected after a valid username is submitted and the password field is shown. Therefore, two-step logins can only be successfully black-box detected using a valid username.

4.2 Submitting credentials to login

After identifying a login area, Shepherd proceeds to submit credentials according to the type of login area (one-step or two-step). If this causes the password input element to disappear, the website status is set to authenticated

. This heuristic is imperfect, as websites may also stop showing password fields in other cases. For example, when the user is blocked or a server error results in a 404 page. To separate such cases from actual logins, the authentication process is followed by a verification process.

4.3 Verifying login status

After submitting credentials, Shepherd evaluates whether the login was successful. Shepherd’s approach to this is similar to previous approaches [MFK16, CTBO14]. The main difference is that previous works relied on manual logins, and were therefore certain of the validity of the credentials. Conversely, Shepherd relies on a source of unverified credentials, and thus cannot assume that the credentials are correct. To improve verification, each verification method is run twice: once with all cookies, and once without any cookies. The test is passed when the first run succeeds and the second run fails. This ensures that the verification method only depends on the site’s cookies. Currently, Shepherd uses two verification methods. The first is to detect a logout button or user identifier on the landing page. The second is to attempt to re-open the login area. This is typically not possible in well-designed sites after the user has logged in.

5 Scanning for session hijacking susceptibility

To validate the effectiveness of Shepherd, we set out to conduct a large-scale experiment to ascertain how many sites are vulnerable to session hijacking. Various previous works have investigated session security, e.g. [MFK16, vAHS17]. However, no study attempted large-scale, fully automatic login. To automate the scan, we simplified the goal and only look for authentication cookies for which the Secure flag is not set. This is sufficient, in case these cookies can indeed be stolen and if these cookies are sufficient for session hijacking. We performed two small experiments to test if these assumptions hold in practice.

An easy way to determine session hijacking susceptibility is to actually hijack the session. In controlled conditions (at the very least: the researcher’s own session is hijacked on another machine, and nothing is done with the session except confirming it is hijacked), a manual attack could be performed ethically. However, it is not clear whether it is possible to automate this approach yet still maintain ethical safeguards. For example, how to ensure that nothing is clicked after logging in, if we cannot be 100% sure that the browser is logged in?

To sidestep this issue, we first investigate whether the question can be simplified, based on the following assumptions:

  1. [I.]

  2. Possession of a victim’s authentication cookie is sufficient to execute a session hijacking attack;

  3. Lack of the cookie flag Secure is sufficient to steal a victim’s cookie.

These assumptions are well-known666Assumption I was even used to automate session hijacking, e.g. by the Firesheep plugin for Firefox. and believed to hold. However, in practice, other security practices may interfere with these. For example, the Hypertext Strict Transport Security HTTP header instructs browsers to only contact a site over an HTTPS channel, and never over an HTTP channel. Thus, lack of a Secure cookie-flag may still be mitigated elsewhere. Similarly, an authentication cookie may be bound to e.g. the IP address of a computer. While this countermeasure is far from perfect, it would defeat assumption I.

5.0.1 Cookie stealing suffices for session hijacking.

We manually tested a handful of sites found in an early trial run of Shepherd. For each tested site, we found that its authentication cookie sufficed to perform a session hijacking attack. Though this was a limited test, it always succeeded. This gives a reasonable degree of assurance that in practice, cookie stealing suffices for session hijacking. All sites investigated in this part of the experiment were sent notice of this, though most did not react or acknowledge reception.

5.0.2 Automatically probing a cookie jar.

We devised a means to automatically probe a victim’s cookie jar for authentication cookies. Similar to Van Acker et al. [vAHS17], we use ARP poisoning and the MitMproxy (http://mitmproxy.org/) to set up a man-in-the-middle. Our man-in-the-middle injects HTML into any unencrypted HTTP response (i.e., not transmitted over HTTPS). Specifically, for each target site, it injects a line into the HTML header as follows:

<link src="http://TARGETSITE/style.css" type="text/css">

By using links to style sheets, we avoid common measures that would interfere, such as blocking third party scripts.

This line triggers the browser to send out an HTTP request (i.e., unencrypted) to the target site. Due to the design of HTTP, cookies are automatically included unless settings (such as the Secure cookie flag) prevent this. Thus, the injection will trigger the victim’s browser to send out an HTTP request to each targeted site, accompanied by session cookies if these are present. Testing this setup with a handful of sites showed that indeed the cookie jar can be easily probed and authentication cookies can be stolen.

From these two experiments we conclude that examining the Secure flag on the authentication cookies is indeed sufficient in practice to determine whether a site is susceptible to session hijacking. In order to use Shepherd to scan for authentication cookies lacking the Secure flag, we must be able to automatically determine which of the cookies are authentication cookies.

5.1 Determining authentication cookies

Previous work attempted to identify authentication cookies by applying heuristics on cookie properties [dRND12, TDK11, BCFK14, NMY11]. Calzavara et al. [CTBO14] found that these approaches either resulted in an over- or under-approximation of which cookies are authentication cookies. They provide an improved algorithm, which is used by Mundada et al. [MFK16] and which forms the basis of our approach. This approach eliminates cookies by assessing the user’s login status. As such, a reliable verification of this status is a necessary pre-condition. The approach is to remove a cookie from the set of cookies and access the site afresh. If the user is then still logged in, the removed cookie was not an authentication cookie and is removed from consideration. As websites may use multiple cookies for authentication, applying this test means testing combinations of cookies. The number of tests to check all combinations quickly becomes infeasible in practice, especially for large-scale measurements. Our current implementation adapts two out of three measures from [CTBO14] to reduce the total number of needed site revisits. Once a working set is found, all supersets can be excluded from further tests, since these sets will work as well. Additionally, if a not working set is found, all subsets can also be eliminated, as these lack at least one cookie needed to authenticate. We measured the number of executions and runtime of this algorithm in our study. The results for performing this algorithm can be found in Section 6.

6 Case studies: session hijacking susceptibility

We tested Shepherd’s ability to perform large-scale, post-login scanning of websites by evaluating session hijacking susceptibility. We scanned over 50,000 websites using credentials from BugMeNot. In addition, we scanned the Alexa Top 10,000 with an extension to login using Facebook’s single sign-on solution.

We performed the BugMeNot study in February and the Facebook study in April 2018. We used two machines hosted in Germany, each running five browser instances in parallel. Shepherd needs an average of 60.1 seconds to process a single site based on our scan with credentials from BugMeNot. This allowed us to process 15,000 sites a day. The Facebook extension can scan 3,000 sites per day. This lower limit is due to websites that heavily use Facebook-related elements. Both scans were completed within one week.

We also measured Shepherd’s performance in determining authentication cookies. In order to keep the runtime acceptable, we did not execute this process for websites with more than 23 cookies. In our study, websites contained an average of 8.2 cookies. Shepherd needed (on average) 11.9 revisits to determine which were authentication cookies. On average, websites in our experiment use 1.51 cookies for authentication, which is close to the observation in [CTBO14].

6.1 Extraction of BugMeNot credentials

For our case study, we sourced credentials from BugMeNot by applying domains from the Alexa Top 1 Million dataset from February 2018. With this approach we extracted 131,345 accounts for 50,439 unique domains. As shown in Figure 3, this dataset covers over 39% of the Alexa Top 10K domains and 18% of the Top 100K respectively. After consulting the IRB, we discarded our attempt to extended this set of credentials with credentials from a prior run in October 2017. The major reason for that is, that this set might contain websites, which have decided to opt-out of BugMeNot.

Figure 3: Distribution of the BugMeNot dataset within the Alexa Top 1 Million.

6.2 Scanner Success Rate

Table 1 shows success rates of the various processes (described in Section 4) Shepherd executes to perform logins. As previously mentioned, the underlying heuristics are not 100% perfect and may occasionally fail to determine the status correctly. To determine bounds on the error rates, we manually evaluated websites previously scanned by Shepherd. This revealed shortcomings and areas for improvement of Shepherd’s approach to logging in. To evaluate Shepherd, we created four sets of 100 websites each from the BugMeNot case study. In the first case, we manually accessed sites, while for cases 2–4 we reviewed automatically created screenshots.

BugMeNot scan Facebook scan
# sites out of # sites out of
Total 50,439 10,000
Sites reached 46,942 50,439 (93.1%) 8,829 10,000 (88.3%)
Login page found 31,543 46,942 (67.1%) 2,057 8,829 (23.3%)
Authenticated 9,903 31,543 (31.4%) 1,915 2,057 (93.1%)
Verified 6,273 9,903 (63.3%) 383 1,915 (20.0%)
Auth cookies found 5,539 6,273 (88.3%) 330 383 (86.2%)

Success rate analysed in corresponding case number.

Table 1: Performance of the BugMeNot and Facebook scans.
Case 1: Finding login pages.

This case concerns websites where Shepherd was unable to find login areas. We evaluated 100 such sites found login areas on 35 sites. 15 sites could not be evaluated, because they were blocked for foreign countries or did not respond. We did not discover login elements on the other 50 sites. Therefore, Shepherd missed out on at most 35 out of 85 sites. While this seems reasonable, this concerns the first stage. Therefore, any improvements in the success rate here will translate into significantly more sites to process further.

Case 2: Authentication failures.

This case concerns sites where Shepherd found a login area and submitted credentials, but could not detect success. Shepherd automatically took screenshots of 100 such sites. We found 55 screenshots indicating incorrect credentials. 30 pages could not be evaluated due to unclear content (23), missing screenshots (6) or inappropriate content. 6 screenshots showed dialogues for captchas and 3 showed 404 error messages. Only on 6 screenshots we saw that Shepherd ended up in a wrong login area (SSO logins: 2, registration pages: 2, wrong forms: 2). Therefore, we conclude that given a login area, the process of credential submission and evaluation of the success of that step performs reasonably well. However, 55 out of 70 sites showed errors due to invalid credentials. Finding a better service with a massive amount of credentials is in our view unlikely777In our search, we found a handful of databases of dubious legality, and others that only included a subset of the credentials of BugMeNot. The most obvious improvement approach is then to use single sign-on credentials.

Case 3: Authenticated, but not verified.

This case concerns sites that successfully pass authentication, but verification of that failed. Verification is supposed to fail when login was not successful. In 60 out of 100 cases, this was the case, underscoring the need to verify whether login is indeed successful. 24 cases showed clear signs that Shepherd entered the post-login stage, while 16 cases could not be verified. In other words: in at least 24% of the examined cases, this process resulted in a false negative. In the BugMeNot scan, 4,488 sites fell in this category. Additional or improved verification methods thus may lead to hundreds of sites more evaluated.

Case 4: Verified.

This case concerns 100 sites which passed verification. Of these, only two sites could not be checked. Two other screenshots showed that the user account was banned. Nevertheless, Shepherd was clearly logged into these sites. For that, we find the verification process to have high accuracy (96%) and therefore have high confidence in all findings on sites marked verified.

In addition, we compare votes from BugMeNot to our success rates in Appendix A. This provides another approach to assess Shepherd’s effectiveness.

We zoomed in on the process to determine the login area. We tracked how successful each of the methods used in this process were. Here, one should keep in mind that these methods are executed sequentially: only if methods 1–4 fail, method 5 is executed. Of the 31,543 domains where a login area was found, method 2 was most successful, finding over half the login areas.

  • Method 1 - Landing page: 6,865

  • Method 2 - URLs: 17,381

  • Method 3 - Clicking elements: 3,495

  • Method 4 - Brute force: 3,126

  • Method 5 - Second level URLs: 676

Overall, we recorded 6,273 successful and verified logins. For 3,630 sites, Shepherd believed it was logged in but failed to verify this. This set may also contain sites where login was indeed successful. In our manual evaluation, the rate where logins were successful for authenticated-but-not-verified sites was 24%. Extrapolating from that experiment to this set gives a rough estimate of about 870 sites where login was indeed successful, despite lack of automated verification.

6.2.1 Performance of Facebook login extension.

For this case study, we divided the Alexa Top 10K into 4 equal sets. In addition, we created two Facebook accounts, one for each machine. The first machine was used to scan the first 3 sets (see top part in table 1), while the second machine scanned only the last set. We observed that the Facebook account for the first machine was blocked at a certain point while scanning the second set. This was due to posting inappropriate content, caused by Shepherd clicking share buttons on visited sites. We adjusted Shepherd to avoid posting content by blacklisting certain types of Facebook URLs. After recovering the blocked account and scanning the third set, we found 55 shared posts on the account (each of which must be due to a successful login). Shepherd misclassified these as logins. Furthermore, we found that 664 third parties were granted access to the used Facebook account. These must originate from one of the 1,915 successful logins. The second Facebook account was also blocked, this time due to suspicious behaviour. Recovery of this account was more involved and therefore omitted. Unlike the other 3 sets, we thus could not verify Shepherd’s results for this set in the Facebook account.

Auth. but not verif. % Verified %
Total 3,630 6,273
HSTS 412 11.4% 905 14.4%
HKPK 37 1.0% 33 0.5%
HttpOnly 2,115 58.3% 3,605 57.5%
Secure 1,221 33.6% 3,613 57.6%
HSTS/secure 125 3.4% 81 1.3%
SameSite 0 0.0% 0 0.0%
Susceptible 2,284 62.9% 2,579 41.4%
Table 2: Security evaluation of the entire BugMeNot data set.
Auth. but not verif. % Verified %
Total 1,532 383
HSTS 391 25.5% 90 23.5%
HKPK 7 0.5% 3 0.8%
HTTPonly 995 64.9% 333 86.9%
Secure 1,015 66.3% 363 94,8%
HSTS/secure 105 6.9% 2 0.5%
SameSite 0 0.0% 0 0.0%
Susceptible 412 26.9% 18 4.7%
Table 3: Security evaluation of the Facebook case study.

7 Analysis of results

Shepherd automatically assesses the security of websites by scanning HTTP headers of a website’s response and cookie flags. Assessing sites without logging in may lead to incorrect conclusions about a website’s security. Therefore, only authenticated and verified sites are considered for assessments. Note that based on our manual validation from Section 6.2, only verified sites were evaluated in a post-login stage with a high confidentiality. In case the authentication cookies could not be determined, all cookies were treated as authentication cookies.

A website is marked as susceptible to session hijacking, if at least one of the following three conditions is true:

  1. [I.]

  2. the website does not set the Secure cookie flag,

  3. the website does not set the Samesite cookie flag, or

  4. the website does not use HSTS.

For verified sites, we find that 2,579 of 6,273 (41.4%) sites are susceptible to session hijacking. A smaller fraction of websites uses HSTS. Of these, 81 sites use HSTS but do not set the secure flag. We also found that the HttpOnly cookie flag was missing on 2,668 (42.5%) websites. For sites scanned with Facebook credentials, we discovered that 18 out of 383 sites (4.7%) are susceptible to session hijacking. Moreover, in the Facebook scan, 50 sites lacked the HttpOnly flag (13.1%).

Tables 2 and 3 show the results of the security evaluation. All sites which were authenticated, but not verified, are summarised in the category authenticated. Sites are only denoted as susceptible when they use none of HSTS, Samesite flag or Secure flag. Sites that do not implement the Secure flag but use HSTS are counted under HSTS/secure.

8 Investigating the BugMeNot Bias

A key feature of the main experiment is that it uses a source of usernames and passwords. However, that source, BugMeNot, has strict rules on what is allowed – for example, credentials from hacking attempts are disallowed. Moreover, site owners can request BugMeNot to never accept credentials for their sites. Thus, sites in BugMeNot are not representative of the whole internet.

To get a better grasp on how biased the BugMeNot dataset is, we compared the results of that experiment with the Facebook experiment. Of course, the latter is also biased (not every site with a login will have a Facebook login), but we assume that these biases are independent of each other. In the comparison, we only use domains out of the Alexa Top 10K, as the Facebook experiment was confined to these. The results are shown in Table 4. In this table, the percentages columns compares entries on that row to the row labeled “total”.

Facebook experiment BugMeNot experiment
Auth. Verification… Auth. Verification…
OK  failed % success % OK  failed % success %
Total 1,915 1,532 383 778 289 489
Secure 1,378 1,015 66.3% 363 94.8% 512 147 50.9% 365 74.6%
HSTS/secure 107 105 6.9% 2 0.5% 17 13 4.5% 4 0.8%
HTTPonly 1,328 995 64.9% 333 86.9% 459 189 65.4% 270 55.2%
Susceptible 430 412 26.9 % 18 4.7% 251 130 45.0 % 121 24.7%
Table 4: Comparison of successfully authenticated sites of two experiments

From this comparison, it is clear that the BugMeNot approach is better engineered: when it authenticates, verification is far more often successful. Furthermore, differences between cookie flags are profound: Secure flag differs by 20%, the HttpOnly flag by 30%. In the BugMeNot experiment, 24.7% of websites where the login was successfully verified, was susceptible to simple session hijacking. However, in the Facebook experiment, this was only 4.7%. Based on this comparison, it is clear that the results of the BugMeNot experiment should not be extrapolated as-is. At the same time, in both experiments, insecure cookies are a common occurrence (at least 24.7% and 4.7%, respectively). We therefore conclude that more effort is needed to secure online authentication processes.

9 Conclusions and Future work

Many previous works have studied the web. Most of these could not look beyond the login barrier on websites. However, logins have become more common. This implies that more and more content that is actually accessed by users is omitted from such studies. Research that attempted to address the post-login world, mostly fell back on manual intervention, to avoid the many challenges with an automatic approach [TDK11].

In this paper, we designed and developed Shepherd, a tool that enables post-login measurements of unknown websites. As login processes are very diverse, automatic logging in cannot achieve full coverage. Moreover, the diversity of the employed login processes implies many design challenges. Previous efforts using automated or semi-automated approaches managed up to 203 sites [RB14, MFK16]. In contrast, in our case study we automatically logged into at least 6,273 sites.

We ran two experiments, one using credentials from an external source (BugMeNot) and one using Facebook single sign-on. These experiments found thousands of websites vulnerable to straightforward session hijacking. However, as the comparison with the Facebook experiment clearly showed, this cannot be extrapolated to other websites. Nevertheless: equally clearly, the results of the BugMeNot experiment cannot be ignored: out of the 6,273 BugMeNot sites where Shepherd could successfully verify login automatically, 2,579 (41.4%) are vulnerable. It is an illusion to think that the problem of unprotected authentication cookies only applies to sites for which BugMeNot has credentials.

9.1 Future work

First of all, we are working to support more single sign-on frameworks and make these procedures more robust. This will allow us to get a broader estimate of the number of sites susceptible to simple hijacking. Secondly, we are planning to expand our study of session security to account for more factors, such as session fixation or CSRF resistance and cookie invalidation security.

A followup work would be to combine logging in with the OpenWPM framework to detect if the privacy of logged-in users is treated equivalently to anonymous users or not.

9.2 Acknowledgments

We would like to thank the Technische Hochschule Köln for allowing us to make use of their computing resources for this project. In addition, we extend our thanks to Jun Pang, Olga Gadyatskaya, Agnieszka Jonker and Jana Polednovà for translating keywords into Chinese, Russian, Polish, French, Spanish, and Czech. Finally, we are grateful for comments we received on earlier versions of this work from Marko van Eekelen, Greg Álpar, the institutional review board of the Open University of the Netherlands and the anonymous reviewers of ESORICS’18 and ICISC’18.

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Appendix A Login success rate compared with BugMeNot votes

User submitted credentials might be invalid or outdated. BugMeNot offers to submit votes about the success of credentials. Separating invalid from valid credentials would help to assess Shepherd’s performance, because this would eliminate the external cause for failed login attempts. However, user submitted votes on its own need not be valid and cannot fully be trusted – yet they do provide some information. For example, we found that a freshly added (invalid) credential receives a 100% success score and one vote. Thus, when correlating votes in BugMeNot with success rates of the Shepherd framework, we ignore credentials with a 100% score and less than 3 votes. We accumulated success rates and vote counts for our dataset (see Table A1).

Authenticated
Category   Entire set   Not authenticated   includ. non verif.   verified only
Domains 42,770 33,012 8,805 5,066
 0 x  30% 14,327 12,581 1,208 408
30 x  70% 17,103 14,045 2,439 1,256
70 x 100% 11,340 6,386 4,558 3,402
Table A1: Correlation of BugMeNot user votes and the result of scanning with Shepherd. All entries with less than 3 votes and 100% were removed.

In some cases, BugMeNot provides multiple credentials for one domain. We focus only on the credentials with the highest success rate. We sorted these into 3 categories to determine the likelihood of successful login (see Fig. 4). As shown in the figure, there is a rough correlation between verified logins and highly rated credentials, as well as the inverse: Shepherd fails in 97.1% for lowly rated credentials.

Figure 4: Correlation of BugMeNot user votes and Shepherd’s results.