The ability to switch Apple IDs on iOS devices poses a security risk for Corporate-Owned, Personally Enabled (COPE) mobile environments. When users opt to retain Keychain entries while transitioning from a corporate Apple ID to a private Apple ID, it results in the merging of passwords to the private Apple ID. This merging poses a significant challenge for organizations that need to maintain strict control over corporate credentials, as enterprise passwords can then be synced with all private devices using the private Apple ID.
Switching Apple IDs on an iOS device involves opening the “Settings” app on an iOS device, navigate to the Apple ID section and opt to sign out of the current Apple ID. This prompts a confirmation step, asking users if they wish to keep a copy of their data on the device or to delete it. This decision is crucial to consider because if the user opts to keep the Keychain data to be stored on the device, this decision will have implications for the passwords associated with the Apple ID currently signing out. Following the sign-out, users can proceed to sign in with the new Apple ID. Once the necessary information is entered and the setup is complete, the iOS device becomes associated with the new Apple ID. The device syncs with the new account, applying apps, data, and settings associated with the updated Apple ID. However, apps installed with the previous Apple ID are still usable.
Keychain Merging and Its Effects
When logging into a website using Safari, a pop-up typically appears, asking if the user likes to store the password for AutoFill. This method simplifies the process of adding accounts to the password store. Alternatively, when creating a new account, users receive an automatically generated strong password. In either case, users can add accounts and passwords in the password store. The passwords are stored in the Keychain. If the user chooses to keep the Keychain data stored on the device during the Apple ID switching, this results in a merging of the stored passwords of the former and the latter configured Apple ID on the device.
COPE environments prioritize the separation of work and personal data, but the Keychain merging while switching Apple IDs on iOS devices undermines this separation. This has to be considered in situations where employees use their personal Apple IDs alongside corporate profiles on the same device. If one Apple ID during the Apple ID switching process is the corporate ID and the other is the private ID, that leads to merging of corporate and personal credentials compromising the security posture of COPE mobile environments and/or the privacy of the user’s private credentials.
Lack of Mitigation Through MDM Restrictions
Currently, there is an absence of Mobile Device Management restrictions to mitigate the unintended consequences of password merging during Apple ID switching. The inability to prevent Keychain merging in COPE environments can have a significant impact for organizations. The only measure is to enforce employee education and awareness regarding the risks associated with Apple ID switching and Keychain merging. Training programs should emphasize the importance of choosing secure options during account transitions and the potential impact on corporate and private data security.
The merging of Keychain entries during Apple ID switching on iOS devices poses a significant security challenge, particularly in COPE mobile environments. Organizations currently need to be proactive in addressing this issue through employee education and awareness programs. Collaboration between organizations, MDM providers and Apple, is needed to develop comprehensive solutions that safeguard corporate credentials and mitigate the risks associated with Apple ID switching.
The number of ways to bypass iOS data flow restrictions meanwhile has further increased, but Apple still does not bother to fix them. So, the question is: How trustworthy are iOS MDM restrictions if even simple tricks to bypass them are not closed by Apple?
In many enterprises, there is the need to separate data of different origins or trust levels. In COPE (company owned, personally enabled) deployments this typically is the separation between private and business apps. Whereas in COBO (company owned, business only) deployments often data of supporting tools (such as travel management) should be separated from confidential enterprise data or other data with higher protection requirements.
The recently new discovered and reported issues are related to the iOS Files app. The first issue is related to the “Recents” Tab of the Files app. When a user had opened any document in a managed app before and now open an unmanaged document in the “Recents” tab of the Files app, the share action incorrectly presents a list of managed apps instead of unmanaged apps. The opposite direction for the bypass (managed to unmanaged app) is the second newly reported issue. Deleting a managed document with the Files app and copying it to an unmanaged app folder (instead of restoring it) removes the MDM restriction and permits the user to open the document in the unmanaged app. This is also shown in our extended demonstration video, now demonstrating five unfixed issues with the iOS data flow restrictions.
Reactions to Reported Issues
We again reported the two new issues to Apple, but also this time, Apple argued that “MDM profiles provide configuration management but do not establish additional security boundaries beyond what iOS and iPadOS have to offer.” A similar argument was also given for the reported MDM restriction bypass of SySS GmbH.
For all these six bypass issues, there are currently no mitigations available. Taking all the current conversations with Apple into account, and as reported issues of 2020 are still not fixed, there seems to be no justifiable reason to believe that our findings will be fixed sooner.
Consequences for Enterprises
MDM restrictions are a vital instrument for securing the usage of iOS devices in enterprise environments. Only thanks to these restrictions, the requirements of enterprises can be fulfilled for a device that is primarily developed for the consumer market. Even with effective MDM restrictions it is still a challenging task for enterprises to keep up with constantly changed and extended features of mobile smartphone operating systems. And often enough, MDM restrictions that would make new features acceptable for enterprises are released only major versions later, if any (e.g., still no possibility to prevent a mix-up of private and enterprise passwords when switching between private and enterprise Apple IDs in COPE deployments, discussed in an upcoming article).
Experiencing the lack of support for fixing multiple reported issues in advertised security features now raises the question: Which other MDM restrictions are insecure? When one follows Apple’s argument, for any other bypass of such MDM restrictions a patch will be rejected and probably already have been rejected before, as MDM restrictions are always just configuration options per definition. This isn’t then only a technical issue, it becomes an issue of trust in supporting enterprises with reliable solutions.
In consequence, the missing patches reduce the possibilities for a secure iOS enterprise deployment in COPE and COBO scenarios.
Apple promotes MDM restrictions that should prevent data flows between managed and unmanaged apps. These restrictions can be bypassed easily and effortlessly using an app that is pre-installed on every new iOS device: the Shortcuts App. This is a finding of our practical tests on iOS 17.1 that should concern all enterprises that rely on these restrictions to use iOS devices in a compliant way with their enterprise policy for personal enabled usage.
Apple promotes the data flow control between managed and unmanaged apps as a solution for data separation between personal and corporate data, “to keep it protected from both attacks and user missteps“. To achieve this protection, many enterprises configure the following Mobile Device Management (MDM) restrictions for the managed iOS devices:
Managed pasteboard. In iOS 15 and iPadOS 15 or later, this restriction helps control the pasting of content between managed and unmanaged destinations. When the restrictions above are enforced, pasting of content is designed to respect the Managed Open In boundary between third-party or Apple apps like Calendar, Files, Mail, and Notes. And with this restriction, apps can’t request items from the pasteboard when the content crosses the managed boundary.
Allow documents from managed sources in unmanaged destinations. Enforcing this restriction helps prevent an organization’s managed sources and accounts from opening documents in a user’s personal destinations. This restriction could prevent a confidential email attachment in your organization’s managed mail account from being opened in any of the user’s personal apps.
With the first restriction, a user should not be able to paste the content of the pasteboard to an unmanaged app, if the content was copied from a managed app, or vice versa. The second restriction applies the same restriction to documents. And in our tests, iOS prevented us from performing such actions, after the MDM restrictions for the test devices have been activated.
However, the iOS Shortcuts App, which is an integral part since iOS 13 and can be used to automate almost any task on iOS, can be abused to bypass such restrictions. The first shortcut (below on the left side) simply reads the clipboard and copies the content back to the global clipboard, which is then also synced with all other devices that the user has logged in with the same Apple account. When the user activates this shortcut, it strips of the information, that the original content was copied from a managed app and so iOS does no longer prevent the paste action to an unmanaged app. Each shortcut can also be combined with a trigger action that can automated the execution of it. So, the bypass of iOS MDM Restrictions can also be automated.
The second example shortcut uses the input of the share sheet to save the content of a document from a managed app. When using this shortcut in the share sheet, iOS prompts for name and location of the file and stores it without the information, that the content originated from a managed app. This way the document can be opened also from unmanaged apps. We created a video for both bypass methods to show the environment and the results in action. But these are only simple examples of the problem. There are many more possibilities to use actions of the Shortcuts App to bypass the iOS MDM restrictions.
Apparently, the Shortcuts App has privileges to access any data from managed apps, although it is not configured as a managed app through the MDM profile. This way the app can access the data, but when the data is saved by the Shortcuts App, it is saved as an unmanaged app. The fix therefore should be to keep the origin information of the content the Shortcuts app processes. This way the app could still access content from managed apps, but the processed content would be marked as originated from a managed app and this way the data separation would be kept intact.
We reported this flaw via Apple’s responsible disclosure process. However, the issue was rejected. We provided a detailed description, screenshots and videos, but also on a second try, where we explained again that the bypass circumvents an advertised security feature of iOS, it was not accepted. We also reached out to Apple via feedbackassistant.apple.com, but did not receive any answer in 2 months. As the current Shortcuts App version 6.1.2 is still vulnerable, we decided to publish the findings to warn enterprises that they cannot rely on iOS data flow restrictions.
A short-term solution would be to delete the iOS Shortcuts App from the devices and block the installation with the MDM blacklist via the bundle ID “com.apple.shortcuts” or to exclude devices with installed Shortcuts App from the domain as non-compliant, depending on the provided functionality of the MDM solution used. However, in the VMware Workspace ONE UEM (AirWatch) MDM tested, the Shortcuts App blacklisting did not prevent the installation nor was the device marked as non-compliant.
Without a proper blocking of the Shortcuts App, company data can currently be sent unintentionally in unmanaged apps through manually used or automated shortcuts and thus leave the company in an uncontrolled manner.
You can visit us on it-sa Expo&Congress, 10. – 12. October in Hall 6, Booth 210 for further details.
Device fingerprinting is a technique that got popular at the end of the 90s by websites, to identify and track users. Apps have in contrast to websites often access to a much wider property range usable for fingerprinting. The unique device identification via fingerprinting across sandbox borders of multiple apps is a relevant privacy issue as it increase the possibilities for tracking users, even without requiring permissions. Many smartphone users are unaware of such practices and possibilities because such activities happen unnoticeably in the background.
Just like each person has an individual fingerprint, electronic devices are also uniquely identifiable. Through manufacturing tolerances, different hardware configurations, software properties and individual software configurability such devices become uniquely identifiable. Depending on the number and the variance of properties used, more or less unique fingerprints can be generated. Typically, several hard- and software device properties are combined through a hashing algorithm to an ideally unique bit sequence.
Motivations for app developers to include tracking libraries are: advertisement, bot detection, account takeover, spam, fraud detection and secure environment detection for payments. Mobile operating systems such as Android and iOS are aware of the user’s transparency through fingerprinting and take steps to empower the user to regain privacy whilst trying to maintain legitimate tracking objectives. The advertisement ID was introduced especially to target device tracking with a resettable ID provided by iOS. It allows advertisers to personalize ads and at the same time give the user options to reset it or hide it for individual apps. Also, Privacy Labels are added to the app store to better explain the app’s data usage to the user. Permissions and Privacy Labels should shed light on the usage of tracking, and it’s purpose during app install and usage. Apple AppStore’s licence agreement also states clear rules on data and identifier usage. However, as shown by Deng et al., many apps exist in the AppStore infringing such rules, working their way around permissions and being dishonest with their Privacy Labels. One increasing way to bypass such restrictions is to use fingerprinting techniques not requiring user consent.
Tracking Possibilities with Fingerprinting
The fingerprint generated by each app containing the same fingerprinting SDK are ideally the same on the same device. The user becomes very transparent by correlating the fingerprint with other data provided during app usage. As an example, one could think of a scenario, pictured in the following figure, where a shopping app, a search engine app and a navigation app all contain the same fingerprinting SDK. All user input of each app like recent purchases, search terms and the device location could be accumulated in the cloud under the same fingerprint. The data collection of each app alone is already a privacy risk to the user, but the combination of different app data makes users fully transparent. Such data is extremely valuable for example for advertisement companies to personally tailor advertisements for individual users.
Example of tracking possibilities with fingerprinting SDKs
Fingerprinting SDKs and Commonly used Properties
To gain insight into the current state of iOS fingerprinting and the latest techniques being used, we identified real iOS fingerprinting SDKs through systematic internet research and conducted manual static and dynamic analysis. The respective SDKs source-code was manually analysed with Ghidra and if possible, the SDK was tested in a minimal app construct. During the manual analysis we judged the SDK as fingerprinter or non-fingerprinter based on the observed behaviour and used properties. This leaves 13 SDKs that we classify as fingerprinters and further analyse in more detail. The SDKs are listed in the following table. We found that all 13 SDKs use the native iOS API to collect device characteristics. Through dynamic analysis of custom-built test apps, we have found that the collection of features shows up through temporal spikes. Most SDKs send the collected device characteristics to a server for further processing, except for OpenIDFA, DFID and DFIDSwift, which are non-commercial products. Additional passive fingerprinting may be performed on the server side. The SDKs that do not include server communication generate a hash on the device and return it. Nevertheless, we argue that fingerprinting is generally only useful when the fingerprint leaves the device. Therefore, it can be assumed that in these cases the caller of the fingerprinting framework handles the network communication. It is obvious from the following table that fingerprinting SDKs share some collected properties, while other properties are exclusively queried by some SDKs. Very commonly used properties are: device model, identifier for vendor (similar to advertisement ID), device name, storage space properties, battery level and different locale identifiers. Other properties like device fonts, commonly used in web based fingerprinters, is rather seldom used. We suppose, due to the low entropy of this value on iOS devices. In conclusion, one can say that fingerprinters usually use multiple properties and don’t rely on a unique identifier provided through the advertisement ID in iOS.
Used device properties by different fingerprinting SDKs
Fingerprinting Access Pattern
We were able to observe access to common iOS APIs gathered in the previous step. Frida hooks were written to log access to respective APIs in a dynamic app analysis on a jailbroken iPhone X (iOS 14.4.2). In the next step, we analysed several top apps from the app store to see if fingerprinting can be observed based on API access pattern during execution.
Firstly, let’s have a look at an example of an app without fingerprinting in the following figure. One can see that different properties were used during the app execution, some less, some more frequently. The accesses to the observed properties came from different caller modules (packages or libraries) from inside the app. Also, the properties were accessed throughout the whole runtime. Access to properties such as timezone and locale are typically required to properly display and run the app and totally legitimate.
Now, let’s look at an app which we judge as fingerprinting in the following figure. One can see that from second 15 to 17, many properties are accessed in a very short timespan. Furthermore, the accesses come from the same caller module called ForterSDK, which is a known fingerprinting SDK. We observed such behaviour for all fingerprinting SDKs: Many properties are collected by the same caller module in a short time frame.
As a proof of concept, we developed a fingerprinting detector to detect fingerprinting on the traces of a dynamic app analysis. The analyser is implemented as a sliding window of a certain width. The window of four seconds width slides over the event timeline and counts the number of accesses to known fingerprinting APIs per caller module. Access numbers above a certain threshold are then justified as fingerprinting. In a first test, we were able to correctly detect fingerprinting in 85% of the cases just by analysing access pattern. As further refinements, machine learning could improve accuracy as well as the inclusion of known fingerprinting SDK properties through a static analysis. Further insights on our used detection mechanism can be read in our extensive paper at ACM, published at the EICC conference 2023.
Fingerprinting as of today is used in many apps. Smartphone users are mostly unaware of the existence and the privacy issues related with such. This research lays the fundamentals to identify device fingerprinting in iOS apps. Future work will be to come up with remediation ideas to balance the privacy interests of smartphone users and fingerprinters.
This research work was supported by the National Research Center for Applied Cybersecurity ATHENE. ATHENE is funded jointly by the German Federal Ministry of Education and Research and the Hessian Ministry of Higher Education, Research and the Arts.
Note: Fingerprinting detection is currently work in progress and not (yet) part of the Appicaptor service.
How has cryptography quality of the top 2000 Android and iOS applications evolved over the past three years? We show an overview of used hashing functions and symmetric encryption algorithms now and then. The results indicate that the majority of apps still use insecure cryptography.
Cryptography algorithms are applicable in many use cases such as for example encryption, hashing, signing. Cryptography has been used since centuries, some cryptography algorithms have been proven to be easily breakable (under certain configurations or conditions) and should thus be avoided. It is not easy for a developer with little cryptographic background to choose secure algorithms and configurations from the plenitude of options. Cryptographic agility is the ability exchange (insecure) cryptography algorithms with secure ones in computer programs.
Analysis Environment & Apps
The analysis results are based on the Appicaptor analysis results of the top 2000 Android and iOS apps. Appicaptor analyzed the current versions of the top 2000 apps along with the three-year-old counterpart of the respective apps. The apps are grouped into top apps from the top 2000 list and business apps uploaded or requested by Appicaptor customers.
Used Hashing Functions
Hashing functions such as MD5 and its predecessors as well as SHA1 are long known to be insecure and prone to collision attacks. It is advised by NIST to move to more secure alternatives like SHA224 or up to SHA512.
The used hashing functions in business apps and the top apps for iOS and Android were analyzed to see the current situation. Afterwards, the 2019 version of the apps is compared to the 2022 version to show the trend for cryptographic agility.
Percentage of apps using the specified hashing functions in top and business apps (2019 VS 2022)
Surprisingly, outdated SHA1 and MD5 hashing is still found in 70% to 80% of the analyzed apps and thus the most used hashing algorithms in iOS and Android in both, the top and business app groups. Even the long outdated MD2 algorithm is still used in 5% to 10% of all apps. These are alarming news regarding security. SHA256 is the only used, yet secure algorithm which is as widespread as MD5 and SHA1.
Comparing hashing in top and business apps, one can see that business apps use less hashing functionality in general.
Looking at the evolution of the top apps on Android and iOS from 2019 to 2022, one can see that the usage of MD5 and SHA1 mostly remains constant with only slight variations. On Android, the SHA2 family and especially SHA512 usage increased. In the case of SHA512, the usage in apps doubled, which is at first sight a positive trend. However, since the usage of outdated algorithms remains constant, one must say, that only more hashing algorithms are used and secure algorithms are not replacing the outdated ones. On iOS, the situation is vice versa: The usage of the SHA2 family even declines which leads to the assumption that less hashing is used on iOS.
In conclusion, one can say that even though Android developers embracing the SHA2 family, outdated hashing functions constantly and heavily remain in Android and iOS apps.
Used Symmetric Encryption Algorithms
As one would expect, AES is the most widely used symmetric encryption algorithm. DES and 3DES are used in around 3% of the analyzed apps. One exception is DES in Android which still seems very popular with a usage in around 12% of the tested apps. Throughout the years, DES and 3DES usage remains mostly constant. However, looking at AES usage over time, one can see that the usage in Android increases in the latest app versions, while at the same time the AES encryption in iOS decreases. Especially on Android, a discrepancy between business and top apps becomes obvious. Business apps seem to use less AES and slightly more DES encryption.
Percentage of apps using the specified encryption functions in top and business apps (2019 VS 2022)
Usage AES in Insecure ECB Mode
Usage of the ECB mode is a very common weakness when applying cryptography. ECB mode outputs the same ciphertext for the same plaintext (when the same key is used). This means that pattern are not hidden very well and one could draw conclusions on the plaintext. With other techniques like CBC or CTR mode, succeeding block’s encryption depend on one another, which introduces randomness and hides pattern. We are aware that under certain conditions the usage of ECB mode is fine, but we advise against using it since secure conditions might easily become insecure during app upgrades, code restructuring or new requirements.
We visualize the usage of insecure ECB mode versus other modes. In the visualization we lay focus on the explicit transition of used secure and insecure modes from 2019 to 2022.
ECB mode usage transitions from 2019 to 2022
Looking at the transitions for Android top apps, we see that 48.9% ECB mode usage in 2019 shrinks to 32.3% in 2022, which is very positive. One can also see that many apps shift from ECB mode to other secure modes. However, a small percentage of apps used secure cryptography in 2019, are now using ECB in 2022.
The situation on iOS looks much different. The transition diagram shows that out of the top apps on iOS, only 12% use ECB mode in 2019 and the majority uses secure alternatives. However, after three years, things didn’t turn out well for iOS apps. With 16% for top apps and 12% on business apps in 2022, more apps are using insecure ECB mode compared to 2019. Even though numbers increased on iOS, ECB usage on Android is still far more widespread, but decreases.
Causes of ECB mode usage
From all observations, we find the transitions from secure (non-ECB) to insecure (ECB) cryptography and vice versa very interesting. Understanding reasons for the transitions could give hints on how developers could be lead towards better cryptography standards. The transitions from ECB to non-ECB and non-ECB to ECB is significantly strong in Android top apps.
Triggers for a change from secure encryption (non-ECB) to insecure encryption (ECB) on Android and iOS
A more in-depth analysis of these apps reveals that in around 90% of the cases, the transition from ECB or towards ECB is triggered by an included third-party library. On iOS in 70% of the cases the transition is triggered through third-party libraries and in 30% of the cases through code changes of the app developer.
Libraries triggering transition insecure cryptography (non-ECB) to secure cryptography (ECB) on AndroidLibraries triggering transition insecure cryptography (ECB) to secure cryptography (non-ECB) on AndroidECB removal comes from library update or library removal from app
Different Android third-party libraries which cause the transition from an insecure to a secure cryptography mode and vice versa were analyzed. The transition from ECB to non-ECB is pretty clear, 98% of the apps discontinued using ECB due to not using Google GMS Advertisement library anymore. In 2% of the apps, the respective library was not identifiable due to obfuscation. The pie charts also show which libraries triggered the ECB usage in 2022 apps. Leading with 29% is Google GMS Advertisement library followed by Icelink (12%), Microsoft Identity (10%) and Apache Commons (10%). Respective apps were deeper analyzed, to see if ECB was introduced through a third-party library update or just by adding a new third-party library with ECB usage. In fact, in 92% of the cases libraries with ECB usage were added and only in 8% of the cases a third-party library update introduced ECB.
This analysis has proven that the majority of apps still use insecure cryptography. The trend over the past years unfortunately shows no significant drift towards secure algorithms on the broad front. Some single aspects like ECB usage on Android point into the right direction. The detailed analysis in finding causes of the ECB usage on Android showed that this flaw is mostly introduced through the usage of third-party libraries during app development.
The contents of this blog post is a condensed version of the award-winning paper published at the international ICISSP 2023 Conference. The full paper can be viewed at Scitepress.
What data is transferred by business apps and how secure is their processing? Our research shows: If your employees use apps arbitrarily, you put your company’s security at risk.
At it-sa 2022, we present our app analysis framework Appicaptor, which you can use to automatically check whether apps are compliant with your company’s IT security demands. New, within the BMBF funded research project PANDERAM developed methods will complement Appicaptor. Among other things, the goal is to identify, evaluate and visualize complex data flows from automated dynamic analyses.
Excerpt dynamic data flow analysis created in BMBF project PANDERAM: Location and advertisementID information flow to third party providers using granted permissions for a weather app
One focus of the ongoing PANDERAM project is to develop an analysis platform using a lightweight approach for automatic data collection from large app sets in order to provide users with information about the IT security quality and privacy of their apps. The technical basis is built upon dynamic analysis of app security properties by applying hooking techniques within a custom-built runtime environment.
Using this approach, the IT security quality of apps is automatically evaluated, including issues that go beyond the aspects observable at communication level such as lack of encryption in local data storage or usage of weak cryptography. By hooking system functions, the evaluation environment also detects access to system resources such as memory card, calendar, contacts, etc. The dynamic approach allows TLS pinning to be switched off, so that transmitted data can be read and evaluated depending on their triggering factors. Furthermore, the analysis platform includes unsupervised operation and usage of apps, which autonomously recognizes specific app operation concepts for the analysis of a large functional scope of apps. Particularly important here are approaches for dealing with login fields, recognition of navigation elements and other interactive elements that must be correctly recognized and operated at app startup in order to enable app functionality.
The example of an Android weather app above shows a first visualization of the analyzed data flows. Highlighted are the location and advertisementID information that are transmitted to third parties when the app is granted the required permissions. For weather apps the example demonstrates the problem, that the user wants to share the location to retrieve the local weather. However, all included third-party libraries get the permission to access and transmit the location information for their purposes as well. Which might not be in the interest of the information owner. Fraunhofer SIT’s Appicaptor specific data flow analysis methods will evaluate the transition of corporate or business data to external parties on that concept.
You’ll find us in hall 6, booth number 6-210 for a demonstration.