An automated, all-in-one mobile application (Android/iOS/Windows) pen-testing, malware analysis, and security assessment framework.
Mobile Security Framework (MobSF) is an automated, all-in-one security testing platform for mobile applications on Android, iOS, and Windows Mobile. It performs static and dynamic analysis to identify vulnerabilities, detect malware, and assess privacy risks, helping developers and security teams secure their mobile apps.
Mobile application developers, security researchers, penetration testers, and DevSecOps teams who need to integrate security testing into their development and deployment pipelines.
Developers choose MobSF for its comprehensive, automated analysis across multiple mobile platforms, its seamless integration into CI/CD workflows, and its open-source nature which provides a powerful, customizable alternative to commercial security tools.
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.
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Supports static and dynamic analysis for Android, iOS, and Windows Mobile apps, including APK, IPA, and APPX binaries, as well as source code.
Offers REST APIs and CLI tools for seamless CI/CD pipeline integration, enabling automated security testing in development environments.
Detects malicious behavior and assesses data privacy risks through automated static and dynamic analysis, highlighted in the README's feature list.
Includes a Slack channel for free discussions, enterprise support options, and a long history of updates, awards, and contributions from collaborators.
Setting up dynamic analysis, especially for iOS, requires additional configuration like emulators or real devices, which can be resource-intensive and time-consuming.
Free support is limited; advanced features, priority requests, and live support require enterprise packages, as noted in the support section.
Windows Mobile analysis is less emphasized compared to Android and iOS, with fewer examples and potentially less robust tooling in the README.
Running dynamic analysis with instrumented testing and emulators demands significant CPU and memory, which may not suit low-resource environments.