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2_background/2_3_static_analysis.typ
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2_background/2_3_static_analysis.typ
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#import "../lib.typ": APK, etal, ART, SDK, DEX, eg,
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#import "../lib.typ": todo, jm-note, jfl-note
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#import "@preview/diagraph:0.3.5": raw-render
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=== Static Analysis <sec:bg-static>
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Static analysis program examine an #APK file without executing it to extract information from it.
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Basic static analysis can include extracting information from the `AndroidManifest.xml` file or decompiling bytecode to Java code with tools like Apktool or Jadx.
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Unfortunately, simply reading the bytecode does not scale.
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To do so, a human analyst is needed, making it complicated to analyse a large number of applications, and even for single applications, the size and complexity of some applications can quickly overwhelm the reverse engineer.
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Control flow analysis is often used to mitigate this issue.
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The idea is to extract the behaviour, the flow, of the application from the bytecode, and to represent it as a graph.
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A graph representation is easier to work with than a list of instructions, and can be used for further analysis.
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Depending on the level of precision required, different types of graphs can be computed.
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The most basic of those graph is the call graph.
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A call graph is a graph where the nodes represent the methods in the application, and the edges reprensent calls from one method to another.
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@fig:bg-fizzbuzz-cg-cfg b) show the call graph of the code in @fig:bg-fizzbuzz-cg-cfg a).
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A more advance control-flow analysis consist in building the control-flow graph.
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This time, instead of methods, the nodes represent instructions, and the edges indicate which instruction can follow which instruction.
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@fig:bg-fizzbuzz-cg-cfg c) represents the control-flow graph of @fig:bg-fizzbuzz-cg-cfg a), with code statement instead of bytecode instructions.
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#todo[Add alt text for @fig:bg-fizzbuzz-cg and @fig:bg-fizzbuzz-cfg]
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#figure({
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set align(center)
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stack(dir: ttb,[
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#figure(
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```java
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public static void fizzBuzz(int n) {
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for (int i = 1; i <= n; i++) {
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if (i % 3 == 0 && i % 5 == 0) {
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Buzzer.fizzBuzz();
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} else if (i % 3 == 0) {
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Buzzer.fizz();
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} else if (i % 5 == 0) {
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Buzzer.buzz();
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} else {
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Log.e("fizzbuzz", String.valueOf(i));
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}
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}
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}
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```,
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supplement: none,
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kind: "bg-fizzbuzz-cg-cfg subfig",
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caption: [a) A Java program],
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) <fig:bg-fizzbuzz-java>], v(2em), stack(dir: ltr, [
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#figure(
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raw-render(```
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digraph {
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rankdir=LR
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"fizzBuzz(int)" -> "Buzzer.fizzBuzz()"
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"fizzBuzz(int)" -> "Buzzer.fizz()"
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"fizzBuzz(int)" -> "Buzzer.buzz()"
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"fizzBuzz(int)" -> "String.valueOf(int)"
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"fizzBuzz(int)" -> "Log.e(String, String)"
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}
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```,
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width: 40%,
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alt: "",
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),
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supplement: none,
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kind: "bg-fizzbuzz-cg-cfg subfig",
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caption: [b) Corresponding Call Graph]
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) <fig:bg-fizzbuzz-cg>],[
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#figure(
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raw-render(```
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digraph {
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l1
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l2
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l3
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l4
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l5
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l6
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l7
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l9
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l1 -> l2
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l2 -> l3
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l3 -> l1
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l2 -> l4
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l4 -> l5
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l5 -> l1
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l4 -> l6
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l6 -> l7
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l7 -> l1
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l6 -> l9
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l9 -> l1
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}
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```,
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labels: (
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"l1": `for (int i = 1; i <= n; i++) {`,
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"l2": `if (i % 3 == 0 && i % 5 == 0) {`,
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"l3": `Buzzer.fizzBuzz();`,
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"l4": `} else if (i % 3 == 0) {`,
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"l5": `Buzzer.fizz();`,
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"l6": `} else if (i % 5 == 0) {`,
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"l7": `Buzzer.buzz();`,
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"l9": `Log.e("fizzbuzz", String.valueOf(i));`,
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),
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width: 50%,
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alt: "",
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),
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supplement: none,
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kind: "bg-fizzbuzz-cg-cfg subfig",
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caption: [c) Corresponding Control-Flow Graph]
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) <fig:bg-fizzbuzz-cfg>]))
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h(1em)},
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supplement: [Figure],
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caption: [Source code for a simple Java method and its Call and Control Flow Graphs],
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)<fig:bg-fizzbuzz-cg-cfg>
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Once the control-flow graph is computed, it can be used to compute data-flows.
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Data-flow analysis, also called taint-tracking, allows to follow the flow of information in the application.
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Be defining a list of methods and fields that can generate critical information (taint sources) and a list of methods that can consume information (taint sink), taint-tracking allows to detect potential data leaks (if a data flow link a taint source and a taint sink).
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For example, `TelephonyManager.getImei()` returns an unique, persistent, device identifier.
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This can be used to identify the user, and it cannot be changed if compromised.
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This make `TelephonyManager.getImei()` a good candidate as a taint source.
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On the other hand, `UrlRequest.start()` send a request to an external server, making it a taint sink.
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If a data-flow is found linking `TelephonyManager.getImei()` to `UrlRequest.start()`, this means the application is potentially leaking a critical information to an external entity, a behavior that is probably not wanted by the user.
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Static analysis is powerful as it allows to detects unwanted behavior in an application even is the behavior does not manifest itself when running the application.
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Hovewer, static analysis tools must overcom many challenges when analysing Android applications.
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/ the Java object-oriented paradigm: A call to a method can in fact correspond to a call to any method overriding the original method in subclasses.
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/ the multiplicity of entry points: Each component of an application can be an entry point for the application.
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/ the event driven architecture: Methods of in the applications can be called when event occur, in unknown order.
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/ the interleaving of native code and bytecode: Native code can be called from bytecode and vice versa, but tools often only handle one of those format.
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/ the potential dynamic code loading: An application can run code that was not originally in the application.
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/ the use of reflection: Methods can be called from their name as a string object, which is difficult to identify statically.
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/ the continual evolution of Android: each new version of Android brings new features that an analysis tools must be aware of.
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For instance, the multi-dex feature presented in @sec:bg-android-code-format was introduced in Android #SDK 21.
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Tools unaware of this feature only analyse the `classes.dex` file an will ignore all other `classes<n>.dex` files.
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#todo[Ca serait bien de souligner Dyn Code Load et Reflection]
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