Android Anti-Reversing Defenses
Overview
General Disclaimer
The lack of any of these measures does not cause a vulnerability - instead, they are meant to increase the app's resilience against reverse engineering and specific client-side attacks.
None of these measures can assure a 100% effectiveness, as the reverse engineer will always have full access to the device and will therefore always win (given enough time and resources)!
For example, preventing debugging is virtually impossible. If the app is publicly available, it can be run on an untrusted device that is under full control of the attacker. A very determined attacker will eventually manage to bypass all the app's anti-debugging controls by patching the app binary or by dynamically modifying the app's behavior at runtime with tools such as Frida.
You can learn more about principles and technical risks of reverse engineering and code modification in these OWASP documents:
Root Detection and Common Root Detection Methods
In the context of anti-reversing, the goal of root detection is to make running the app on a rooted device a bit more difficult, which in turn blocks some of the tools and techniques reverse engineers like to use. Like most other defenses, root detection is not very effective by itself, but implementing multiple root checks that are scattered throughout the app can improve the effectiveness of the overall anti-tampering scheme.
For Android, we define "root detection" a bit more broadly, including custom ROMs detection, i.e., determining whether the device is a stock Android build or a custom build.
In the following section, we list some common root detection methods you'll encounter. You'll find some of these methods implemented in the OWASP UnCrackable Apps for Android that accompany the OWASP Mobile Testing Guide.
Root detection can also be implemented through libraries such as RootBeer.
SafetyNet
SafetyNet is an Android API that provides a set of services and creates profiles of devices according to software and hardware information. This profile is then compared to a list of accepted device models that have passed Android compatibility testing. Google recommends using the feature as "an additional in-depth defense signal as part of an anti-abuse system".
How exactly SafetyNet works is not well documented and may change at any time. When you call this API, SafetyNet downloads a binary package containing the device validation code provided from Google, and the code is then dynamically executed via reflection. An analysis by John Kozyrakis showed that SafetyNet also attempts to detect whether the device is rooted, but exactly how that's determined is unclear.
To use the API, an app may call the SafetyNetApi.attest
method (which returns a JWS message with the Attestation Result) and then check the following fields:
ctsProfileMatch
: If 'true', the device profile matches one of Google's listed devices.basicIntegrity
: If 'true', the device running the app likely hasn't been tampered with.nonces
: To match the response to its request.timestampMs
: To check how much time has passed since you made the request and you got the response. A delayed response may suggest suspicious activity.apkPackageName
,apkCertificateDigestSha256
,apkDigestSha256
: Provide information about the APK, which is used to verify the identity of the calling app. These parameters are absent if the API cannot reliably determine the APK information.
The following is a sample attestation result:
ctsProfileMatch Vs basicIntegrity
The SafetyNet Attestation API initially provided a single value called basicIntegrity
to help developers determine the integrity of a device. As the API evolved, Google introduced a new, stricter check whose results appear in a value called ctsProfileMatch
, which allows developers to more finely evaluate the devices on which their app is running.
In broad terms, basicIntegrity
gives you a signal about the general integrity of the device and its API. Many Rooted devices fail basicIntegrity
, as do emulators, virtual devices, and devices with signs of tampering, such as API hooks.
On the other hand, ctsProfileMatch
gives you a much stricter signal about the compatibility of the device. Only unmodified devices that have been certified by Google can pass ctsProfileMatch
. Devices that will fail ctsProfileMatch
include the following:
Devices that fail
basicIntegrity
Devices with an unlocked bootloader
Devices with a custom system image (custom ROM)
Devices for which the manufacturer didn't apply for, or pass, Google certification
Devices with a system image built directly from the Android Open Source Program source files
Devices with a system image distributed as part of a beta or developer preview program (including the Android Beta Program)
Recommendations when using SafetyNetApi.attest
Create a large (16 bytes or longer) random number on your server using a cryptographically-secure random function so that a malicious user can not reuse a successful attestation result in place of an unsuccessful result
Trust APK information (
apkPackageName
,apkCertificateDigestSha256
andapkDigestSha256
) only if the value ofctsProfileMatch
is true.The entire JWS response should be sent to your server, using a secure connection, for verification. It isn't recommended to perform the verification directly in the app because, in that case, there is no guarantee that the verification logic itself hasn't been modified.
The
verify
method only validates that the JWS message was signed by SafetyNet. It doesn't verify that the payload of the verdict matches your expectations. As useful as this service may seem, it is designed for test purposes only, and it has very strict usage quotas of 10,000 requests per day, per project which will not be increased upon request. Hence, you should refer SafetyNet Verification Samples and implement the digital signature verification logic on your server in a way that it doesn't depend on Google's servers.The SafetyNet Attestation API gives you a snapshot of the state of a device at the moment when the attestation request was made. A successful attestation doesn't necessarily mean that the device would have passed attestation in the past, or that it will in the future. It's recommended to plan a strategy to use the least amount of attestations required to satisfy the use case.
To prevent inadvertently reaching your
SafetyNetApi.attest
quota and getting attestation errors, you should build a system that monitors your usage of the API and warns you well before you reach your quota so you can get it increased. You should also be prepared to handle attestation failures because of an exceeded quota and avoid blocking all your users in this situation. If you are close to reaching your quota, or expect a short-term spike that may lead you to exceed your quota, you can submit this form to request short or long-term increases to the quota for your API key. This process, as well as the additional quota, is free of charge.
Follow this checklist to ensure that you've completed each of the steps needed to integrate the SafetyNetApi.attest
API into the app.
Programmatic Detection
File existence checks
Perhaps the most widely used method of programmatic detection is checking for files typically found on rooted devices, such as package files of common rooting apps and their associated files and directories, including the following:
Detection code also often looks for binaries that are usually installed once a device has been rooted. These searches include checking for busybox and attempting to open the su binary at different locations:
Checking whether su
is on the PATH also works:
File checks can be easily implemented in both Java and native code. The following JNI example (adapted from rootinspector) uses the stat
system call to retrieve information about a file and returns "1" if the file exists.
Executing su
and other commands
Another way of determining whether su
exists is attempting to execute it through the Runtime.getRuntime.exec
method. An IOException will be thrown if su
is not on the PATH. The same method can be used to check for other programs often found on rooted devices, such as busybox and the symbolic links that typically point to it.
Checking running processes
Supersu-by far the most popular rooting tool-runs an authentication daemon named daemonsu
, so the presence of this process is another sign of a rooted device. Running processes can be enumerated with the ActivityManager.getRunningAppProcesses
and manager.getRunningServices
APIs, the ps
command, and browsing through the /proc
directory. The following is an example implemented in rootinspector:
Checking installed app packages
You can use the Android package manager to obtain a list of installed packages. The following package names belong to popular rooting tools:
Checking for writable partitions and system directories
Unusual permissions on system directories may indicate a customized or rooted device. Although the system and data directories are normally mounted read-only, you'll sometimes find them mounted read-write when the device is rooted. Look for these filesystems mounted with the "rw" flag or try to create a file in the data directories.
Checking for custom Android builds
Checking for signs of test builds and custom ROMs is also helpful. One way to do this is to check the BUILD tag for test-keys, which normally indicate a custom Android image. Check the BUILD tag as follows:
Missing Google Over-The-Air (OTA) certificates is another sign of a custom ROM: on stock Android builds, OTA updates Google's public certificates.
Anti-Debugging
Debugging is a highly effective way to analyze runtime app behavior. It allows the reverse engineer to step through the code, stop app execution at arbitrary points, inspect the state of variables, read and modify memory, and a lot more.
Anti-debugging features can be preventive or reactive. As the name implies, preventive anti-debugging prevents the debugger from attaching in the first place; reactive anti-debugging involves detecting debuggers and reacting to them in some way (e.g., terminating the app or triggering hidden behavior). The "more-is-better" rule applies: to maximize effectiveness, defenders combine multiple methods of prevention and detection that operate on different API layers and are well distributed throughout the app.
As mentioned in the "Reverse Engineering and Tampering" chapter, we have to deal with two debugging protocols on Android: we can debug on the Java level with JDWP or on the native layer via a ptrace-based debugger. A good anti-debugging scheme should defend against both types of debugging.
JDWP Anti-Debugging
In the chapter "Reverse Engineering and Tampering", we talked about JDWP, the protocol used for communication between the debugger and the Java Virtual Machine. We showed that it is easy to enable debugging for any app by patching its manifest file, and changing the ro.debuggable
system property which enables debugging for all apps. Let's look at a few things developers do to detect and disable JDWP debuggers.
Checking the Debuggable Flag in ApplicationInfo
We have already encountered the android:debuggable
attribute. This flag in the Android Manifest determines whether the JDWP thread is started for the app. Its value can be determined programmatically, via the app's ApplicationInfo
object. If the flag is set, the manifest has been tampered with and allows debugging.
isDebuggerConnected
While this might be pretty obvious to circumvent for a reverse engineer, you can use isDebuggerConnected
from the android.os.Debug
class to determine whether a debugger is connected.
The same API can be called via native code by accessing the DvmGlobals global structure.
Timer Checks
Debug.threadCpuTimeNanos
indicates the amount of time that the current thread has been executing code. Because debugging slows down process execution, you can use the difference in execution time to guess whether a debugger is attached.
Messing with JDWP-Related Data Structures
In Dalvik, the global virtual machine state is accessible via the DvmGlobals
structure. The global variable gDvm holds a pointer to this structure. DvmGlobals
contains various variables and pointers that are important for JDWP debugging and can be tampered with.
For example, setting the gDvm.methDalvikDdmcServer_dispatch function pointer to NULL crashes the JDWP thread:
You can disable debugging by using similar techniques in ART even though the gDvm variable is not available. The ART runtime exports some of the vtables of JDWP-related classes as global symbols (in C++, vtables are tables that hold pointers to class methods). This includes the vtables of the classes JdwpSocketState
and JdwpAdbState
, which handle JDWP connections via network sockets and ADB, respectively. You can manipulate the behavior of the debugging runtime by overwriting the method pointers in the associated vtables (archived).
One way to overwrite the method pointers is to overwrite the address of the function jdwpAdbState::ProcessIncoming
with the address of JdwpAdbState::Shutdown
. This will cause the debugger to disconnect immediately.
Traditional Anti-Debugging
On Linux, the ptrace
system call is used to observe and control the execution of a process (the tracee) and to examine and change that process' memory and registers. ptrace
is the primary way to implement system call tracing and breakpoint debugging in native code. Most JDWP anti-debugging tricks (which may be safe for timer-based checks) won't catch classical debuggers based on ptrace
and therefore, many Android anti-debugging tricks include ptrace
, often exploiting the fact that only one debugger at a time can attach to a process.
Checking TracerPid
When you debug an app and set a breakpoint on native code, Android Studio will copy the needed files to the target device and start the lldb-server which will use ptrace
to attach to the process. From this moment on, if you inspect the status file of the debugged process (/proc/<pid>/status
or /proc/self/status
), you will see that the "TracerPid" field has a value different from 0, which is a sign of debugging.
Remember that this only applies to native code. If you're debugging a Java/Kotlin-only app the value of the "TracerPid" field should be 0.
This technique is usually applied within the JNI native libraries in C, as shown in Google's gperftools (Google Performance Tools)) Heap Checker implementation of the IsDebuggerAttached
method. However, if you prefer to include this check as part of your Java/Kotlin code you can refer to this Java implementation of the hasTracerPid
method from Tim Strazzere's Anti-Emulator project.
When trying to implement such a method yourself, you can manually check the value of TracerPid with ADB. The following listing uses Google's NDK sample app hello-jni (com.example.hellojni) to perform the check after attaching Android Studio's debugger:
You can see how the status file of com.example.hellojni (PID=11657) contains a TracerPID of 11839, which we can identify as the lldb-server process.
Using Fork and ptrace
You can prevent debugging of a process by forking a child process and attaching it to the parent as a debugger via code similar to the following simple example code:
With the child attached, further attempts to attach to the parent will fail. We can verify this by compiling the code into a JNI function and packing it into an app we run on the device.
Attempting to attach to the parent process with gdbserver fails with an error:
You can easily bypass this failure, however, by killing the child and "freeing" the parent from being traced. You'll therefore usually find more elaborate schemes, involving multiple processes and threads as well as some form of monitoring to impede tampering. Common methods include
forking multiple processes that trace one another,
keeping track of running processes to make sure the children stay alive,
monitoring values in the
/proc
filesystem, such as TracerPID in/proc/pid/status
.
Let's look at a simple improvement for the method above. After the initial fork
, we launch in the parent an extra thread that continually monitors the child's status. Depending on whether the app has been built in debug or release mode (which is indicated by the android:debuggable
flag in the manifest), the child process should do one of the following things:
In release mode: The call to ptrace fails and the child crashes immediately with a segmentation fault (exit code 11).
In debug mode: The call to ptrace works and the child should run indefinitely. Consequently, a call to
waitpid(child_pid)
should never return. If it does, something is fishy and we would kill the whole process group.
The following is the complete code for implementing this improvement with a JNI function:
Again, we pack this into an Android app to see if it works. Just as before, two processes show up when we run the app's debug build.
However, if we terminate the child process at this point, the parent exits as well:
To bypass this, we must modify the app's behavior slightly (the easiest ways to do so are patching the call to _exit
with NOPs and hooking the function _exit
in libc.so
). At this point, we have entered the proverbial "arms race": implementing more intricate forms of this defense as well as bypassing it are always possible.
File Integrity Checks
There are two topics related to file integrity:
Code integrity checks: In the "Tampering and Reverse Engineering on Android" chapter, we discussed Android's APK code signature check. We also saw that determined reverse engineers can easily bypass this check by re-packaging and re-signing an app. To make this bypassing process more involved, a protection scheme can be augmented with CRC checks on the app bytecode, native libraries, and important data files. These checks can be implemented on both the Java and the native layer. The idea is to have additional controls in place so that the app only runs correctly in its unmodified state, even if the code signature is valid.
The file storage integrity checks: The integrity of files that the application stores on the SD card or public storage and the integrity of key-value pairs that are stored in
SharedPreferences
should be protected.
Sample Implementation - Application Source Code
Integrity checks often calculate a checksum or hash over selected files. Commonly protected files include
AndroidManifest.xml,
class files *.dex,
native libraries (*.so).
The following sample implementation from the Android Cracking blog calculates a CRC over classes.dex
and compares it to the expected value.
Sample Implementation - Storage
When providing integrity on the storage itself, you can either create an HMAC over a given key-value pair (as for the Android SharedPreferences
) or create an HMAC over a complete file that's provided by the file system.
When using an HMAC, you can use a bouncy castle implementation or the AndroidKeyStore to HMAC the given content.
Complete the following procedure when generating an HMAC with BouncyCastle:
Make sure BouncyCastle or SpongyCastle is registered as a security provider.
Initialize the HMAC with a key (which can be stored in a keystore).
Get the byte array of the content that needs an HMAC.
Call
doFinal
on the HMAC with the bytecode.Append the HMAC to the bytearray obtained in step 3.
Store the result of step 5.
Complete the following procedure when verifying the HMAC with BouncyCastle:
Make sure that BouncyCastle or SpongyCastle is registered as a security provider.
Extract the message and the HMAC-bytes as separate arrays.
Repeat steps 1-4 of the procedure for generating an HMAC.
Compare the extracted HMAC-bytes to the result of step 3.
When generating the HMAC based on the Android Keystore, then it is best to only do this for Android 6.0 (API level 23) and higher.
The following is a convenient HMAC implementation without AndroidKeyStore
:
Another way to provide integrity is to sign the byte array you obtained and add the signature to the original byte array.
Detection of Reverse Engineering Tools
The presence of tools, frameworks and apps commonly used by reverse engineers may indicate an attempt to reverse engineer the app. Some of these tools can only run on a rooted device, while others force the app into debugging mode or depend on starting a background service on the mobile phone. Therefore, there are different ways that an app may implement to detect a reverse engineering attack and react to it, e.g. by terminating itself.
You can detect popular reverse engineering tools that have been installed in an unmodified form by looking for associated application packages, files, processes, or other tool-specific modifications and artifacts. In the following examples, we'll discuss different ways to detect the Frida instrumentation framework, which is used extensively in this guide. Other tools, such as Substrate and Xposed, can be detected similarly. Note that DBI/injection/hooking tools can often be detected implicitly, through runtime integrity checks, which are discussed below.
For instance, in its default configuration on a rooted device, Frida runs on the device as frida-server. When you explicitly attach to a target app (e.g. via frida-trace or the Frida REPL), Frida injects a frida-agent into the memory of the app. Therefore, you may expect to find it there after attaching to the app (and not before). If you check /proc/<pid>/maps
you'll find the frida-agent as frida-agent-64.so:
The other method (which also works for non-rooted devices) consists of embedding a frida-gadget into the APK and forcing the app to load it as one of its native libraries. If you inspect the app memory maps after starting the app (no need to attach explicitly to it) you'll find the embedded frida-gadget as libfrida-gadget.so.
Looking at these two traces that Frida lefts behind, you might already imagine that detecting those would be a trivial task. And actually, so trivial will be bypassing that detection. But things can get much more complicated. The following table shortly presents a set of some typical Frida detection methods and a short discussion on their effectiveness.
Some of the following detection methods are presented in the article "The Jiu-Jitsu of Detecting Frida" by Berdhard Mueller (archived). Please refer to it for more details and for example code snippets.
Method | Description | Discussion |
---|---|---|
Checking the App Signature | In order to embed the frida-gadget within the APK, it would need to be repackaged and resigned. You could check the signature of the APK when the app is starting (e.g. GET_SIGNING_CERTIFICATES since API level 28) and compare it to the one you pinned in your APK. | This is unfortunately too trivial to bypass, e.g. by patching the APK or performing system call hooking. |
Check The Environment For Related Artifacts | Artifacts can be package files, binaries, libraries, processes, and temporary files. For Frida, this could be the frida-server running in the target (rooted) system (the daemon responsible for exposing Frida over TCP). Inspect the running services ( | Since Android 7.0 (API level 24), inspecting the running services/processes won't show you daemons like the frida-server as it is not being started by the app itself. Even if it would be possible, bypassing this would be as easy just renaming the corresponding Frida artifact (frida-server/frida-gadget/frida-agent). |
Checking For Open TCP Ports | The frida-server process binds to TCP port 27042 by default. Check whether this port is open is another method of detecting the daemon. | This method detects frida-server in its default mode, but the listening port can be changed via a command line argument, so bypassing this is a little too trivial. |
Checking For Ports Responding To D-Bus Auth |
| This is a fairly robust method of detecting |
Scanning Process Memory for Known Artifacts | Scan the memory for artifacts found in Frida's libraries, e.g. the string "LIBFRIDA" present in all versions of frida-gadget and frida-agent. For example, use | This method is a bit more effective, and it is difficult to bypass with Frida only, especially if some obfuscation has been added and if multiple artifacts are being scanned. However, the chosen artifacts might be patched in the Frida binaries. Find the source code on Berdhard Mueller's GitHub. |
Please remember that this table is far from exhaustive. We could start talking about named pipes (used by frida-server for external communication), detecting trampolines (indirect jump vectors inserted at the prologue of functions), which would help detecting Substrate or Frida's Interceptor but, for example, won't be effective against Frida's Stalker; and many other, more or less, effective detection methods. Each of them will depend on whether you're using a rooted device, the specific version of the rooting method and/or the version of the tool itself. Further, the app can try to make it harder to detect the implemented protection mechanisms by using various obfuscation techniques. At the end, this is part of the cat and mouse game of protecting data being processed on an untrusted environment (an app running in the user device).
It is important to note that these controls are only increasing the complexity of the reverse engineering process. If used, the best approach is to combine the controls cleverly instead of using them individually. However, none of them can assure a 100% effectiveness, as the reverse engineer will always have full access to the device and will therefore always win! You also have to consider that integrating some of the controls into your app might increase the complexity of your app and even have an impact on its performance.
Emulator Detection
In the context of anti-reversing, the goal of emulator detection is to increase the difficulty of running the app on an emulated device, which impedes some tools and techniques reverse engineers like to use. This increased difficulty forces the reverse engineer to defeat the emulator checks or utilize the physical device, thereby barring the access required for large-scale device analysis.
There are several indicators that the device in question is being emulated. Although all these API calls can be hooked, these indicators provide a modest first line of defense.
The first set of indicators are in the file build.prop
.
You can edit the file build.prop
on a rooted Android device or modify it while compiling AOSP from source. Both techniques will allow you to bypass the static string checks above.
The next set of static indicators utilize the Telephony manager. All Android emulators have fixed values that this API can query.
Keep in mind that a hooking framework, such as Xposed or Frida, can hook this API to provide false data.
Runtime Integrity Verification
Controls in this category verify the integrity of the app's memory space to defend the app against memory patches applied during runtime. Such patches include unwanted changes to binary code, bytecode, function pointer tables, and important data structures, as well as rogue code loaded into process memory. Integrity can be verified by:
comparing the contents of memory or a checksum over the contents to good values,
searching memory for the signatures of unwanted modifications.
There's some overlap with the category "detecting reverse engineering tools and frameworks", and, in fact, we demonstrated the signature-based approach in that chapter when we showed how to search process memory for Frida-related strings. Below are a few more examples of various kinds of integrity monitoring.
Detecting Tampering with the Java Runtime
This detection code is from the dead && end blog.
Detecting Native Hooks
By using ELF binaries, native function hooks can be installed by overwriting function pointers in memory (e.g., Global Offset Table or PLT hooking) or patching parts of the function code itself (inline hooking). Checking the integrity of the respective memory regions is one way to detect this kind of hook.
The Global Offset Table (GOT) is used to resolve library functions. During runtime, the dynamic linker patches this table with the absolute addresses of global symbols. GOT hooks overwrite the stored function addresses and redirect legitimate function calls to adversary-controlled code. This type of hook can be detected by enumerating the process memory map and verifying that each GOT entry points to a legitimately loaded library.
In contrast to GNU ld
, which resolves symbol addresses only after they are needed for the first time (lazy binding), the Android linker resolves all external functions and writes the respective GOT entries immediately after a library is loaded (immediate binding). You can therefore expect all GOT entries to point to valid memory locations in the code sections of their respective libraries during runtime. GOT hook detection methods usually walk the GOT and verify this.
Inline hooks work by overwriting a few instructions at the beginning or end of the function code. During runtime, this so-called trampoline redirects execution to the injected code. You can detect inline hooks by inspecting the prologues and epilogues of library functions for suspect instructions, such as far jumps to locations outside the library.
Obfuscation
The chapter "Mobile App Tampering and Reverse Engineering" introduces several well-known obfuscation techniques that can be used in mobile apps in general.
Android apps can implement some of those obfuscation techniques using different tooling. For example, ProGuard offers an easy way to shrink and obfuscate code and to strip unneeded debugging information from the bytecode of Android Java apps. It replaces identifiers, such as class names, method names, and variable names, with meaningless character strings. This is a type of layout obfuscation, which doesn't impact the program's performance.
Decompiling Java classes is trivial, therefore it is recommended to always applying some basic obfuscation to the production bytecode.
Learn more about Android obfuscation techniques:
"Security Hardening of Android Native Code" by Gautam Arvind
"APKiD: Fast Identification of AppShielding Products" by Eduardo Novella
Using ProGuard
Developers use the build.gradle file to enable obfuscation. In the example below, you can see that minifyEnabled
and proguardFiles
are set. Creating exceptions to protect some classes from obfuscation (with -keepclassmembers
and -keep class
) is common. Therefore, auditing the ProGuard configuration file to see what classes are exempted is important. The getDefaultProguardFile('proguard-android.txt')
method gets the default ProGuard settings from the <Android SDK>/tools/proguard/
folder.
Further information on how to shrink, obfuscate, and optimize your app can be found in the Android developer documentation.
When you build your project using Android Studio 3.4 or Android Gradle plugin 3.4.0 or higher, the plugin no longer uses ProGuard to perform compile-time code optimization. Instead, the plugin uses the R8 compiler. R8 works with all of your existing ProGuard rules files, so updating the Android Gradle plugin to use R8 should not require you to change your existing rules.
R8 is the new code shrinker from Google and was introduced in Android Studio 3.3 beta. By default, R8 removes attributes that are useful for debugging, including line numbers, source file names, and variable names. R8 is a free Java class file shrinker, optimizer, obfuscator, and pre-verifier and is faster than ProGuard, see also an Android Developer blog post for further details. It is shipped with Android's SDK tools. To activate shrinking for the release build, add the following to build.gradle:
The file proguard-rules.pro
is where you define custom ProGuard rules. With the flag -keep
you can keep certain code that is not being removed by R8, which might otherwise produce errors. For example to keep common Android classes, as in our sample configuration proguard-rules.pro
file:
You can define this more granularly on specific classes or libraries in your project with the following syntax:
Obfuscation often carries a cost in runtime performance, therefore it is usually only applied to certain very specific parts of the code, typically those dealing with security and runtime protection.
Device Binding
The goal of device binding is to impede an attacker who tries to both copy an app and its state from device A to device B and continue executing the app on device B. After device A has been determined trustworthy, it may have more privileges than device B. These differential privileges should not change when an app is copied from device A to device B.
Before we describe the usable identifiers, let's quickly discuss how they can be used for binding. There are three methods that allow device binding:
Augmenting the credentials used for authentication with device identifiers. This make sense if the application needs to re-authenticate itself and/or the user frequently.
Encrypting the data stored in the device with the key material which is strongly bound to the device can strengthen the device binding. The Android Keystore offers non-exportable private keys which we can use for this. When a malicious actor would extract such data from a device, it wouldn't be possible to decrypt the data, as the key is not accessible. Implementing this, takes the following steps:
Generate the key pair in the Android Keystore using
KeyGenParameterSpec
API.Generating a secret key for AES-GCM:
Encrypt the authentication data and other sensitive data stored by the application using a secret key through AES-GCM cipher and use device specific parameters such as Instance ID, etc. as associated data:
Encrypt the secret key using the public key stored in Android Keystore and store the encrypted secret key in the private storage of the application.
Whenever authentication data such as access tokens or other sensitive data is required, decrypt the secret key using private key stored in Android Keystore and then use the decrypted secret key to decrypt the ciphertext.
Use token-based device authentication (Instance ID) to make sure that the same instance of the app is used.
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