Mini-Module
Summary
This module focuses on automating the security testing process for Android applications. Building on knowledge from earlier modules, it introduces tools and frameworks that enable rapid, repeatable, and scalable analysis, minimizing human error and improving coverage.
In this module, you will:
- Analyze APK's with MobSF and it's intuitive web interface
- Use Quark Engine for rule-based malware detection
- Leverage Drozer to find overexposed app components and simulate malicious Intents
- Explore Frida's instrumentation power through the Objection toolkit
- Automate dynamic behavioral analysis with the Medusa framework
As you work through the module, you will see example commands and command output for the various topics introduced. It is worth reproducing as many of these examples as possible to reinforce further the concepts introduced in each section. You can do this in the Pwnbox provided in the interactive sections, an emulated or physical Android device as directed, or your own virtual machine.
You can start and stop the module at any time and pick up where you left off. There is no time limit or "grading", but you must complete all of the exercises and the skills assessment to receive the maximum number of cubes and have this module marked as complete in any paths you have chosen.
The module is classified as "Medium" and assumes a working knowledge of static and dynamic analysis techniques for Android applications.
Having completed the modules listed below is sufficient for successfully completing this module:
Tools and Methodology
In previous modules, we focused on manually analyzing and exploiting mobile applications, using both static and dynamic techniques. While manual analysis offers flexibility and deep insight, it can be time-consuming and prone to human error. Automation flips the script, leveraging tools and frameworks to perform these tasks quickly, consistently, and with less oversight.
Just as the Android OS has evolved and matured over time, so has the tooling. Today, there's a broad spectrum of automated tools and frameworks, supporting everything from static code review and malware detection to runtime analysis and API interaction. These modern, automated tools are the subject this module: covering their setup, execution, and how to interpret their results.
Before we dive in, let's take a moment to weigh the strengths and limitations of automated testing.
Pros and Cons of Automated Testing
| Advantage | Description |
|---|---|
Speed |
Automated tools scan code much faster than humans, rapidly finding security flaws and other bugs. |
Repeatability |
Tests are executed the same way every time, ensuring consistent results and making it easier to track improvements or regressions in an app's security over time. |
Known Vulnerabily Detection |
Automated tools typically come equipped with databases of known vulnerabilities. If any of these are present in the application, they are promply identified. |
Cost Efficiency |
Automating the testing process reduces the manpower and time required for security testing, which in turn lowers costs. |
| Disadvantage | Description |
|---|---|
Lack of Context Awareness |
Automated tools can miss issues that require human intuition or business logic understanding. |
Setup and Maintenance Overhead |
Setting up automated tests can be complicated and time-consuming. Also, regular updates are typically required as application features evolve and new threats emerge. |
Risk of Over-Reliance |
Solely relying on automated testing can be risky as it might miss complex vulnerabilities that require human insight to detect. |
False Positives/Negatives |
Automated tools often rely on predefined patterns and rules to spot vulnerabilities, which can lead to a higher incidence of both false positives and false negatives. This can complicate the analysis and may require additional manual verification to ensure accuracy. |
Tools for Android Penetration Testing Automation
While there are many fantastic automation tools available, we have selected a few of the most well-proven and widely-adopted tools to focus on.
| Tool | Description |
|---|---|
| MobSF (Mobile Security Framework) | A comprehensive security testing framework that performs static and dynamic analysis on Android apps. Ideal for automated baseline testing. |
| Quark Engine | Signature-based malware detection system that analyzes APK internals for suspicious behavior. |
| Drozer | A framework for exploring an application's IPC attack surface. Requires an agent be installed on the device. |
| Objection | A Frida-based runtime exploration toolkit. Capable of injecting hooks and inspecting app behavior without requiring access to source code. |
| Medusa | A modular framework designed for automated dynamic analysis of mobile apps, with support for both Android and iOS. |
Methodology of Automated Testing
The chart below outlines a typical workflow for automated mobile testing. As you work through each section and its respective labs, take a moment to consider how each phase of the workflow maps to the challenges you're addressing, and how the tool you're using could be applied in a real-world scenario.
| Phase | Description |
|---|---|
Tool Selection |
Choose tools aligned with your target vulnerabilities, factoring in threat complexity and testing objectives. |
Environment Setup |
Install and configure your testing tools. Prepare the Android device (e.g., ensure root access or specific OS version) as required by the toolchain. |
Component Enumeration |
Gather package names, exported components (activities, services, receivers, providers), and third-party modules. |
Tool Configuration & Execution |
Parameterize the tools using the enumerated data, then execute them to begin the scanning or testing process. |
Result Analysis |
Review and interpret the findings. Discard false positives and prioritize issues based on impact and exploitability. |
Remediation and Re-testing |
Patch the confirmed vulnerabilities and re-test to verify the fixes, ensuring that issues are resolved effectively. |
Note that we will not explicitly cover remediation/re-testing or secure coding practices in this module. Those topics will be addressed in a separate track.
