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Framework
Framework
Mobile Security

Mobile-Security-Framework-MobSF

by MobSF

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3.6Kforks
587watchers
Updated about 1 month ago
About

MobSF is an all-in-one automated framework for mobile application security testing, malware analysis, and security assessment.

Mobile Security Framework (MobSF) is an automated, all-in-one mobile application (Android/iOS/Windows) pen-testing, malware analysis and security assessment framework capable of performing static and dynamic analysis.

Primary Use Case

MobSF is primarily used by security researchers and developers for conducting comprehensive security assessments of mobile applications across Android, iOS, and Windows platforms. It is ideal for integrating into DevSecOps or CI/CD pipelines to automate security testing processes.

Key Features
  • Automated static and dynamic analysis
  • Support for Android, iOS, and Windows mobile applications
  • Integration with DevSecOps and CI/CD pipelines
  • REST APIs and CLI tools for automation
  • Interactive instrumented testing
  • Runtime data and network traffic analysis

Installation

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

Usage

>_ docker pull opensecurity/mobile-security-framework-mobsf:latest

Pull the latest MobSF Docker image.

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

Run the MobSF Docker container and expose it on port 8000.

Security Frameworks
Reconnaissance
Initial Access
Execution
Persistence
Defense Evasion
Usage Insights
  • Repurposing: MobSF can be used to educate developers on secure coding practices by analyzing their code and providing security feedback, thus serving as a training tool.
  • Chaining: Combine MobSF with a network traffic analysis tool like Wireshark to correlate app behavior with network anomalies, providing a comprehensive view of potential threats.
  • Evasion/Detection: Attackers might attempt to bypass MobSF by using obfuscation techniques. To detect such attempts, integrate MobSF with a machine learning model trained to recognize patterns of obfuscation.
  • Data Fusion: Correlate MobSF's static and dynamic analysis results with threat intelligence feeds to identify known vulnerabilities and exploits in real-time, enhancing threat detection capabilities.
  • Automation: Integrate MobSF into a CI/CD pipeline with automated alerting systems that notify developers of security issues in real-time, reducing the time to remediation and improving SOC efficiency.

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Security Profile
Red Team80%
Blue Team30%
Purple Team50%
Details
LicenseGNU General Public License v3.0
LanguageJavaScript
Open Issues1546
Topics
static-analysis
dynamic-analysis
mobsf
android-security
mobile-security
windows-mobile-security
ios-security
api-testing
web-security
malware-analysis