Getting Started with Mobile Security Framework (MobSF) for Mobile App Security Testing

Mobile application security is no longer optional—it’s essential. Whether you’re an Android, iOS, or Windows mobile developer, integrating automated security assessments into your CI/CD pipeline can drastically improve your app’s resilience against attacks. Enter MobSF (Mobile Security Framework)—an all-in-one toolkit for performing static and dynamic analysis of mobile apps.

In this guide, we’ll walk through setting up MobSF using Docker on macOS with Colima and demonstrate how to conduct both static and dynamic analysis.

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🚀 What is MobSF?

Mobile Security Framework (MobSF) is an open-source, automated mobile application pentesting, malware analysis, and security assessment tool. It supports:

• Static & dynamic analysis

• Mobile binaries: .apk, .ipa, .appx, .xapk

• Source code (zipped)

• REST APIs for CI/CD or DevSecOps integration

Whether you’re running tests during development or before release, MobSF provides valuable insights into the security posture of your app.

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🛠 Prerequisites

Before starting, ensure the following tools are installed on your macOS system:

• Colima – Docker Desktop alternative for macOS

• Docker

Installation Commands:

brew install colima

brew install docker

colima start

🧪 Running MobSF with Docker

Once Colima and Docker are set up, launch MobSF using the following command:

docker run -it --rm -p 8000:8000 opensecurity/mobile-security-framework-mobsf:latest

🌐 Accessing the MobSF Dashboard

After launching MobSF, the terminal logs will include a line like:

Listening at: http://127.0.0.1:8000

Copy this URL into your browser to open the MobSF dashboard.

Uploading an App:

Simply drag and drop your APK/IPA file into the dashboard to begin static analysis.

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🧾 Static Analysis

Once uploaded, MobSF automatically scans the app and generates a security report. Monitor the Docker logs to verify successful completion or identify potential issues during analysis.

Sample Reports:

• AppSec Scorecard for Prod Build (v2.9.8)

• Full Static Analysis Report

✅ These reports help developers and security teams identify code-level vulnerabilities, permission misuses, and more.

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🔍 Dynamic Analysis

MobSF’s dynamic analysis enables runtime behavior assessment, allowing you to detect malicious operations or insecure runtime behaviors.

🔧 Requirements:

• Emulator without Google Play Store

• API level ≤ 28 (Android 9)

Step 1: Start Emulator

Navigate to your SDK tools directory and run:

cd ~/Library/Android/sdk/tools

./emulator -avd Pixel_5_API_28 -writable-system -no-snapshot

To list available AVDs:

emulator -list-avds

Step 2: Launch MobSF with Emulator Identifier

Find your emulator’s ID using:

adb devices

Then start MobSF with the emulator bound:

docker run -e MOBSF_ANALYZER_IDENTIFIER="emulator-5554" -it --rm -p 8000:8000 opensecurity/mobile-security-framework-mobsf:latest

Step 3: Start Dynamic Analysis

In the MobSF UI:

• Go to Dynamic Analyzer

• Click Start Dynamic Analysis

MobSF will initiate an interactive test session connected to your emulator.

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✅ Final Thoughts

MobSF is a powerful and developer-friendly framework for mobile app security. With minimal setup, it provides:

• Actionable security insights

• Seamless CI/CD integration

• Both static and dynamic testing capabilities

By integrating MobSF into your development lifecycle, you ensure your mobile applications are secure, compliant, and robust.

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📎 Useful Links

• MobSF GitHub Repo

• MobSF Docker Image

• Colima Documentation

🔍 Tools and Technologies I Use for Digital Forensics Investigations

Digital forensics plays a critical role in modern cybersecurity — whether it’s responding to a data breach, investigating insider threats, or performing incident analysis after suspicious behavior. In my work as a security-minded engineer and DevSecOps practitioner, I’ve frequently had to identify, collect, and analyze digital evidence across endpoints, servers, and cloud environments.

In this blog post, I’ll walk you through the tools and technologies I rely on to conduct effective digital forensics investigations — categorized by use case.

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🧠 What Is Digital Forensics?

At its core, digital forensics is about identifying, preserving, analyzing, and reporting on digital data in a way that’s legally sound and technically accurate. The goal is to reconstruct events, identify malicious activity, and support security incident response.

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🧰 My Go-To Tools for Digital Forensics Investigations

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🗂️ Disk & File System Analysis

These tools help examine hard drives, deleted files, system metadata, and more:

• Autopsy (The Sleuth Kit) – A GUI-based forensic suite for analyzing disk images, file recovery, and timelines.

• FTK Imager – For creating and previewing forensic images without altering the original evidence.

• dd / dc3dd – Command-line tools to create low-level forensic disk images in Linux environments.

• EnCase (Basic familiarity) – A commercial powerhouse in forensic investigations, used primarily for legal-grade evidence analysis.

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🧬 Memory Forensics

Memory (RAM) often holds short-lived but critical evidence, like injected malware, live sessions, or loaded processes.

• Volatility Framework – Extracts details like running processes, DLLs, command history, network activity, and more from memory dumps.

• Rekall – An alternative memory analysis framework focused on automation and deep system state inspection.

✅ I’ve used Volatility to trace injected PowerShell payloads and enumerate hidden processes in live incident simulations.

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🌐 Network Forensics

Capturing and analyzing network traffic is essential for spotting data exfiltration, command-and-control activity, or lateral movement.

• Wireshark – Industry standard for packet analysis and protocol dissection.

• tcpdump – Lightweight CLI tool to capture traffic in headless environments or remote systems.

• NetworkMiner – Parses PCAP files to extract files, sessions, and credentials automatically.

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📊 Log & Timeline Analysis

Understanding what happened — and when — is key to reconstructing incidents.

• Timesketch – A timeline analysis tool for visualizing and collaborating on event data.

• Log2Timeline (Plaso) – Converts log files, browser histories, and system events into structured timelines.

• Sysinternals Suite – Includes gems like Procmon, PsExec, and Autoruns for Windows incident response.

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🧪 Malware Analysis (Static & Dynamic)

Understanding what a file does — before or while it runs — helps detect advanced threats and APT tools.

• Ghidra – Powerful open-source reverse engineering tool from the NSA for analyzing executables.

• x64dbg / OllyDbg – Popular debuggers for inspecting Windows executables.

• Hybrid Analysis / VirusTotal – Cloud-based tools to scan files and observe sandbox behavior.

• Cuckoo Sandbox – An open-source automated sandbox for observing malware behavior in a VM.

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☁️ Cloud & Endpoint Forensics

Modern investigations often span cloud platforms and remote endpoints:

• AWS CloudTrail, GuardDuty – Audit user and API activity in cloud environments.

• Microsoft Azure Defender – For cloud-native threat detection and log correlation.

• CrowdStrike Falcon / SentinelOne – Endpoint Detection and Response (EDR) tools for retrieving artifacts, hunting threats, and isolating compromised machines.

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🧰 Scripting & Automation

Scripting accelerates collection, triage, and analysis — especially in large-scale environments.

• Python – I use it to build custom Volatility plugins, PCAP parsers, or automate alert triage.

• Bash / PowerShell – For live memory dumps, log gathering, process inspection, and rapid automation.

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🧩 MITRE ATT&CK & DFIR Methodology

I map artifacts and behaviors to MITRE ATT&CK techniques (e.g., T1055 – Process Injection) to align with industry standards and communicate findings effectively.

I also follow established methodologies like:

• SANS DFIR process

• NIST 800-61 Incident Handling Guide

• Custom playbooks for containment, eradication, and recovery

✅ Summary: Digital Forensics Tools I Use

🔹 Disk & File System Analysis

• Autopsy (Sleuth Kit) – GUI-based forensic suite

• FTK Imager – Create and inspect forensic images

• dd / dc3dd – Low-level disk imaging on Linux

• EnCase – Commercial tool for deep disk investigations (basic familiarity)

🔹 Memory Forensics

• Volatility – Extract processes, DLLs, and sessions from RAM dumps

• Rekall – Advanced volatile memory analysis

🔹 Network Forensics

• Wireshark – Protocol and packet analysis

• tcpdump – Command-line traffic capture

• NetworkMiner – Extracts files and sessions from PCAP files

🔹 Log & Timeline Analysis

• Timesketch – Timeline visualization and correlation

• Plaso (log2timeline) – Converts raw logs into a forensic timeline

• Sysinternals Suite – Live system inspection (Procmon, PsExec, Autoruns)

🔹 Malware Analysis

• Ghidra – Static reverse engineering

• x64dbg / OllyDbg – Debuggers for binary inspection

• Hybrid Analysis / VirusTotal – Behavioral analysis and threat intel

• Cuckoo Sandbox – Automated dynamic malware analysis

🔹 Cloud & Endpoint Forensics

• AWS CloudTrail / GuardDuty – Monitor API and security activity

• Microsoft Defender / Azure Logs – Cloud-native alerting and forensics

• CrowdStrike Falcon / SentinelOne – EDR tools for endpoint activity and IOC collection

🔹 Scripting & Automation

• Python – For custom plugins, log parsers, automation

• Bash / PowerShell – For system triage, memory dumps, and log collection

🔹 Methodology

• Align findings with MITRE ATT&CK

• Follow structured DFIR frameworks like SANS, NIST 800-61, and custom playbooks

🎯 Final Thoughts

Digital forensics isn’t just for breach responders — it’s a key skill for DevSecOps, SDETs, and any security-conscious engineer. Whether you’re building incident response workflows, simulating attacks, or validating your EDR, knowing how to collect and interpret evidence makes you far more effective.

📱 Mobile App Testing: 10 Critical Test Scenarios You Can’t Miss (That Go Beyond Web UI Testing)

When it comes to testing mobile applications, the challenges go far beyond what typical web UI testing entails. Mobile apps must work flawlessly across a fragmented ecosystem of devices, screen sizes, OS versions, sensors, network conditions—and still deliver a high-performance experience. That’s why test engineers must design test cases that account for mobile-specific conditions that web-based apps don’t encounter.

In this post, we’ll break down the 10 critical mobile app test cases that every QA engineer should prioritize—and explain how they differ from traditional web UI testing.

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✅ 1. Installation & Launch

Unlike web apps, mobile apps must be installed, upgraded, and uninstalled through OS-specific stores like Google Play or Apple App Store.

Test Cases:

• App installs/uninstalls cleanly on all supported devices.

• Launches successfully after a clean install or version upgrade.

• First-launch behavior (onboarding, permission prompts) works without failure.

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🌐 2. Device & OS Compatibility

Mobile ecosystems are highly fragmented. You must ensure compatibility across OS versions, hardware specs, and screen dimensions.

Test Cases:

• Verify app functionality on Android 10–14 and iOS 14–17.

• Check responsiveness across tablets, foldables, and small-screen phones.

• Test on low-RAM or budget devices (to catch memory issues).

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📶 3. Network Conditions

Mobile users are always switching between 5G, Wi-Fi, and even no network. Your app must handle this gracefully.

Test Cases:

• App behaves predictably with no internet or low bandwidth.

• Test auto-retries for failed API calls due to timeouts.

• Switching from Wi-Fi to mobile data mid-session doesn’t break functionality.

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🔄 4. Background & Resume Behavior

A mobile app should maintain state and not crash when interrupted by a phone call or switching to another app.

Test Cases:

• App resumes gracefully from background state.

• Data entry is preserved when the user switches away and returns.

• Proper behavior after a cold restart or after device reboot.

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🔋 5. Battery & Performance

Performance testing on mobile goes beyond responsiveness—it’s also about battery and resource consumption.

Test Cases:

• No excessive battery drain during idle or active use.

• Monitor CPU/memory usage over time (watch for leaks).

• Measure cold and warm start times.

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🔐 6. Permission Handling

Mobile apps rely on permissions to access hardware features. You must test both granting and denying permissions.

Test Cases:

• App only requests necessary permissions.

• Behavior is graceful when permissions are denied or revoked.

• Scoped storage compliance (Android 11+) is in place.

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🔔 7. Push Notifications

Push notifications are a core engagement channel and must work across all app states.

Test Cases:

• Push received when app is in background or killed.

• Tapping the notification leads to correct app screen.

• Notifications respect user opt-in/opt-out settings.

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📲 8. Gestures & UI Flexibility

Mobile users interact via gestures and virtual keyboards, making UX more dynamic than web.

Test Cases:

• UI responds correctly to swipes, taps, long presses, and pinch-to-zoom.

• Keyboard overlays don’t hide important input fields.

• Smooth adaptation to dark mode, orientation changes (portrait ↔ landscape).

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🔐 9. Security Testing

Security is non-negotiable, especially with personal data or financial transactions involved.

Test Cases:

• Secure storage for sensitive data (e.g., keystore/token vault).

• No sensitive logs left in logcat or crash logs.

• Behavior on rooted/jailbroken devices is safely restricted.

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📊 10. Analytics & Store Compliance

Apps often embed SDKs for analytics and crash reporting, and must comply with store policies.

Test Cases:

• Verify Firebase, GA, or Crashlytics events are firing correctly.

• App follows Play Store / App Store policy (e.g., no deprecated APIs).

• Correct versioning and metadata shown in store listing.

  
  

  🧪 Final Thoughts

  If you’re only testing your mobile app like a web app, you’re missing half the picture. Mobile brings unique challenges and requires a deeper, device-aware test strategy. The 10 critical mobile test areas above should form the core of your test planning, especially for high-scale production apps used across a variety of devices and conditions.

Generative AI in Software Testing

Generative AI (GenAI) is poised to fundamentally transform the software development lifecycle (SDLC)—especially in software testing. As applications grow in complexity and release cycles shorten, traditional testing methods fall short. GenAI offers a game-changing solution: dynamically generating test cases, identifying risks, and optimizing testing with minimal human input.

Key benefits include:

• Faster test execution

• Enhanced coverage

• Cost reduction

• Improved defect detection

Despite challenges like data quality, integration, and skill gaps, the future of software testing is inseparably linked to GenAI, paving the way toward autonomous and hyper-personalized testing.

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🚀 Main Themes & Tools You Can Use

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1. The Critical Need for GenAI in Modern Software Testing

Why GenAI? Traditional testing can’t keep pace with:

• Complex modern architectures (microservices, containers, cloud-native)

  • GenAI predicts failure points using historical data and real-time scenarios.

  • 🛠️ Tool Example: Diffblue Cover — generates unit tests for Java code using AI.

• Agile & CI/CD Release Pressure

  • According to the World Quality Report 2023, 63% of enterprises face test automation scalability issues.

  • 🛠️ Tool Example: Testim by Tricentis — uses AI to accelerate test creation and maintenance.

• Missed Edge Cases

  • GenAI ensures coverage by analyzing user behavior and generating test cases automatically.

  • 🛠️ Tool Example: Functionize — AI-powered test creation based on user journeys.

• High Manual Effort

  • GenAI generates and updates test scripts autonomously.

  • 🛠️ Tool Example: Mabl — self-healing, low-code test automation platform.

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2. Core Capabilities and Benefits of GenAI in Testing

Capability	Impact
Accelerated Test Execution	Speeds up releases
Enhanced Test Coverage	Covers functional, UI, and edge cases
Reduced Script Maintenance	AI auto-updates outdated tests
Cost Efficiency	Fewer resources, less manual work
Improved Defect Detection	Finds bugs early via predictive analytics

🛠️ Tool Reference: Appvance IQ — uses AI to improve defect detection and test coverage.

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3. Key Applications of GenAI in Software Testing

✅ Automated Test Case Generation

• Analyzes code logic, results, and behavior to generate meaningful test cases.

• 🛠️ Tool: Testsigma — auto-generates and maintains tests using NLP and AI.

🔧 Self-Healing Test Automation

• Automatically adapts to UI or logic changes.

• 🛠️ Tools:

  • Testim

  • Mabl

🧪 Test Data Generation & Management

• Creates compliant synthetic data simulating real-world conditions.

• 🛠️ Tools:

  • Tonic.ai — privacy-safe synthetic test data

  • Datomize — dynamic data masking & synthesis

🔍 Defect Prediction & Anomaly Detection

• Identifies defect-prone areas before they affect production.

• 🛠️ Tool: Appvance IQ

🔁 Optimizing Regression Testing

• Prioritizes relevant tests for code changes.

• 🛠️ Tool: Applitools — AI-driven visual testing and regression optimization.

✍️ NLP for Test Case Creation

• Converts natural language into executable tests.

• 🛠️ Tool: TestRigor — plain English to automated test scripts.

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4. Challenges in Implementing GenAI

Challenge	Description
Data Availability & Quality	Poor data → inaccurate test generation
Tool Integration	Legacy tools may lack AI support
Skill Gap	Requires upskilling QA teams in AI/ML
False Positives	Over-testing may need human review

🛠️ Solution Suggestion: Use platforms like Katalon Studio that offer GenAI plugins with low-code/no-code workflows to reduce technical barriers.

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5. The Future of GenAI in Software Testing

🤖 Autonomous Testing

• Self-designing, executing, and analyzing test frameworks.

• 🛠️ Tool: Functionize

🔄 AI-Augmented DevOps

• Integrated CI/CD with AI-based code quality checks and rollback mechanisms.

• 🛠️ Tool: Harness Test Intelligence — AI-powered testing orchestration in pipelines.

🎯 Hyper-Personalized Testing

• Tailors tests to real user behavior and preferences.

• 🛠️ Tool: Testim Mobile — for AI-driven UX optimization and mobile test personalization.

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🧩 Conclusion

Generative AI isn’t just an enhancement — it’s becoming a necessity for QA teams aiming to keep pace in a high-velocity development environment.

By combining automation, intelligence, and adaptability, GenAI can enable faster releases, fewer bugs, and more robust software.

✅ Start exploring tools like Testim, Appvance IQ, Mabl, Functionize, and Applitools today to get a head start on the future of intelligent testing.

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💬 Let’s Discuss:

Have you implemented GenAI tools in your QA process? What has been your experience with tools like TestRigor, Tonic.ai, or Mabl?

👇 Drop your thoughts or tool recommendations in the comments.

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