performing-android-app-static-analysis-with-mobsf

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/performing-android-app-static-analysis-with-mobsf
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summary

Performs automated static analysis of Android applications using Mobile Security Framework (MobSF) to identify hardcoded secrets, insecure permissions, vulnerable components, weak cryptography, and code-level security flaws without executing the application. Use when assessing Android APK/AAB files for security vulnerabilities before deployment, during penetration testing, or as part of CI/CD security gates. Activates for requests involving Android static analysis, MobSF scanning, APK security assessment, or mobile application code review.

skill.md
name
performing-android-app-static-analysis-with-mobsf
description
'Performs automated static analysis of Android applications using Mobile Security Framework (MobSF) to identify hardcoded secrets, insecure permissions, vulnerable components, weak cryptography, and code-level security flaws without executing the application. Use when assessing Android APK/AAB files for security vulnerabilities before deployment, during penetration testing, or as part of CI/CD security gates. Activates for requests involving Android static analysis, MobSF scanning, APK security assessment, or mobile application code review. '
domain
cybersecurity
subdomain
mobile-security
author
mahipal
tags
- mobile-security - android - mobsf - static-analysis - owasp-mobile - penetration-testing
version
1.0.0
license
Apache-2.0
nist_csf
- PR.PS-01 - PR.AA-05 - ID.RA-01 - DE.CM-09

Performing Android App Static Analysis with MobSF

When to Use

Use this skill when:

  • Conducting security assessment of Android APK or AAB files before production release
  • Integrating automated mobile security scanning into CI/CD pipelines
  • Performing initial triage of Android applications during penetration testing engagements
  • Reviewing third-party Android applications for supply chain security risks

Do not use this skill as a replacement for manual code review or dynamic analysis -- MobSF static analysis catches pattern-based vulnerabilities but misses runtime logic flaws.

Prerequisites

  • MobSF v4.x installed via Docker (docker pull opensecurity/mobile-security-framework-mobsf) or local setup
  • Target Android APK, AAB, or source code ZIP
  • Python 3.10+ for MobSF REST API integration
  • JADX decompiler (bundled with MobSF) for Java/Kotlin source recovery
  • Network access to MobSF web interface (default: http://localhost:8000)

Workflow

Step 1: Deploy MobSF and Obtain API Key

Launch MobSF using Docker for isolated, reproducible scanning:

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

Retrieve the REST API key from the MobSF web interface at http://localhost:8000/api_docs or from the startup console output. The API key enables programmatic scanning.

Step 2: Upload APK for Static Analysis

Upload the target APK using the MobSF REST API:

curl -F "file=@target_app.apk" http://localhost:8000/api/v1/upload \
  -H "Authorization: <API_KEY>"

Response includes the hash identifier used for subsequent API calls. MobSF automatically decompiles the APK using JADX, extracts the AndroidManifest.xml, and indexes all resources.

Step 3: Trigger and Retrieve Static Scan Results

Initiate the static scan and retrieve results:

# Trigger scan
curl -X POST http://localhost:8000/api/v1/scan \
  -H "Authorization: <API_KEY>" \
  -d "scan_type=apk&file_name=target_app.apk&hash=<FILE_HASH>"

# Retrieve JSON report
curl -X POST http://localhost:8000/api/v1/report_json \
  -H "Authorization: <API_KEY>" \
  -d "hash=<FILE_HASH>"

Step 4: Analyze Critical Findings

MobSF static analysis covers these categories mapped to OWASP Mobile Top 10 2024:

Manifest Analysis (M8 - Security Misconfiguration):

  • Exported activities, services, receivers, and content providers without permission guards
  • android:debuggable="true" left enabled
  • android:allowBackup="true" enabling data extraction via ADB
  • Missing android:networkSecurityConfig for certificate pinning

Code Analysis (M1 - Improper Credential Usage):

  • Hardcoded API keys, passwords, and tokens in Java/Kotlin source
  • Insecure SharedPreferences usage for storing sensitive data
  • Weak or broken cryptographic implementations (ECB mode, static IV, hardcoded keys)

Network Security (M5 - Insecure Communication):

  • Missing certificate pinning configuration
  • Custom TrustManagers that accept all certificates
  • Cleartext HTTP traffic allowed without exception domains

Binary Analysis (M7 - Insufficient Binary Protections):

  • Missing ProGuard/R8 obfuscation
  • Native library vulnerabilities (stack canaries, NX bit, PIE)
  • Debugger detection absence

Step 5: Generate and Export Reports

Export findings in multiple formats for stakeholder communication:

# PDF report
curl -X POST http://localhost:8000/api/v1/download_pdf \
  -H "Authorization: <API_KEY>" \
  -d "hash=<FILE_HASH>" -o report.pdf

# JSON for programmatic processing
curl -X POST http://localhost:8000/api/v1/report_json \
  -H "Authorization: <API_KEY>" \
  -d "hash=<FILE_HASH>" -o report.json

Step 6: Integrate into CI/CD Pipeline

Add MobSF scanning as a build gate:

# GitHub Actions example
- name: MobSF Static Analysis
  run: |
    UPLOAD=$(curl -s -F "file=@app/build/outputs/apk/release/app-release.apk" \
      http://mobsf:8000/api/v1/upload -H "Authorization: $MOBSF_API_KEY")
    HASH=$(echo $UPLOAD | jq -r '.hash')
    curl -s -X POST http://mobsf:8000/api/v1/scan \
      -H "Authorization: $MOBSF_API_KEY" \
      -d "scan_type=apk&file_name=app-release.apk&hash=$HASH"
    SCORE=$(curl -s -X POST http://mobsf:8000/api/v1/scorecard \
      -H "Authorization: $MOBSF_API_KEY" -d "hash=$HASH" | jq '.security_score')
    if [ "$SCORE" -lt 60 ]; then exit 1; fi

Key Concepts

TermDefinition
Static AnalysisExamination of application code and resources without executing the program; catches structural and pattern-based vulnerabilities
APK DecompilationProcess of recovering Java/Kotlin source from compiled Dalvik bytecode using tools like JADX or apktool
AndroidManifest.xmlConfiguration file declaring app components, permissions, and security attributes; primary target for manifest analysis
Certificate PinningTechnique binding an app to specific server certificates to prevent man-in-the-middle attacks via rogue CAs
ProGuard/R8Code obfuscation and shrinking tools that make reverse engineering more difficult by renaming classes and removing unused code

Tools & Systems

  • MobSF: Automated mobile security analysis framework supporting static and dynamic analysis of Android/iOS apps
  • JADX: Dex-to-Java decompiler for recovering readable source code from Android APK files
  • apktool: Tool for reverse engineering Android APK files, decoding resources to near-original form
  • Android Lint: Google's static analysis tool for Android-specific code quality and security issues
  • Semgrep: Pattern-based static analysis engine with mobile-specific rule packs for custom vulnerability detection

Common Pitfalls

  • Ignoring false positives: MobSF flags patterns like password in variable names even when not storing actual credentials. Triage all HIGH findings manually before reporting.
  • Missing obfuscated code: Static analysis accuracy drops significantly against obfuscated apps. Supplement with dynamic analysis for apps using DexGuard or custom packers.
  • Outdated MobSF rules: Security rules evolve with Android API levels. Ensure MobSF is updated to match the target app's targetSdkVersion.
  • Skipping native code analysis: MobSF analyzes Java/Kotlin but has limited coverage of native C/C++ libraries. Use checksec and manual review for .so files.
how to use performing-android-app-static-analysis-with-mobsf

How to use performing-android-app-static-analysis-with-mobsf on Cursor

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1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add performing-android-app-static-analysis-with-mobsf
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/performing-android-app-static-analysis-with-mobsf

The skills CLI fetches performing-android-app-static-analysis-with-mobsf from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

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4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/performing-android-app-static-analysis-with-mobsf

Reload or restart Cursor to activate performing-android-app-static-analysis-with-mobsf. Access the skill through slash commands (e.g., /performing-android-app-static-analysis-with-mobsf) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Exploratory Data Analysis

Quickly understand datasets, identify patterns, and generate insights

Example

Analyze CSV with 100K rows, identify outliers, visualize correlations, suggest hypotheses

Reduce EDA time from hours to minutes, uncover insights faster

Data Cleaning & Transformation

Write scripts to clean messy data, handle missing values, normalize formats

Example

Generate Python/SQL to fix date formats, impute missing values, remove duplicates

Automate 80% of data preprocessing work

Statistical Analysis

Perform hypothesis testing, regression, and statistical modeling

Example

Run A/B test analysis, calculate confidence intervals, interpret p-values

Get statistically sound analysis without PhD in statistics

Data Visualization

Create charts, dashboards, and visual reports

Example

Generate matplotlib/seaborn code for time series plots, distribution charts, heatmaps

Build presentation-ready visualizations 3x faster

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Python environment (pandas, numpy, matplotlib) or SQL database access
  • Basic understanding of data analysis concepts
  • Sample datasets for testing skill capabilities

Time Estimate

20-40 minutes to set up and run first analysis

Installation Steps

  1. 1.Install data analysis skill using provided command
  2. 2.Prepare a sample dataset (CSV, JSON, or database connection)
  3. 3.Start with descriptive statistics: 'Summarize this dataset'
  4. 4.Progress to visualization: 'Create a scatter plot of X vs Y'
  5. 5.Advanced analysis: 'Run linear regression and interpret results'
  6. 6.Validate outputs: check calculations, verify visualizations make sense
  7. 7.Document analysis workflow for reproducibility

Common Pitfalls

  • Not validating statistical assumptions before applying tests
  • Accepting visualizations without checking data accuracy
  • Overlooking data quality issues (missing values, outliers)
  • Misinterpreting correlation as causation
  • Using wrong statistical test for data distribution
  • Not considering sample size and statistical power

Best Practices

✓ Do

  • +Always validate data quality before analysis
  • +Check statistical assumptions (normality, independence, etc.)
  • +Visualize data before running statistical tests
  • +Document analysis steps for reproducibility
  • +Cross-validate findings with domain experts
  • +Use skill for initial exploration, then dive deeper manually
  • +Save generated code for reuse on similar datasets

✗ Don't

  • Don't trust analysis without verifying data quality
  • Don't apply statistical tests without checking assumptions
  • Don't make business decisions solely on AI-generated analysis
  • Don't ignore outliers without investigating cause
  • Don't skip data validation and sanity checks
  • Don't use for mission-critical financial or medical analysis without expert review

💡 Pro Tips

  • Describe data context: 'This is user behavior data from e-commerce site'
  • Ask for interpretation: 'What does this correlation mean for business?'
  • Request multiple approaches: 'Show 3 ways to handle missing data'
  • Combine AI analysis with domain expertise for best insights
  • Use for rapid prototyping, then refine analysis manually

When to Use This

✓ Use When

Use for exploratory data analysis, data cleaning, statistical testing, visualization prototyping, and learning new analysis techniques. Best for initial exploration and rapid insights.

✗ Avoid When

Avoid for mission-critical financial analysis, medical research requiring regulatory compliance, production ML models, or when deep statistical expertise is required for nuanced interpretation.

Learning Path

  1. 1Basic: descriptive statistics, data cleaning, simple visualizations
  2. 2Intermediate: hypothesis testing, regression, correlation analysis
  3. 3Advanced: time series analysis, clustering, predictive modeling
  4. 4Expert: causal inference, experimental design, advanced statistical methods

Discussion

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general reviews

Ratings

4.625 reviews
  • Ava White· Dec 12, 2024

    Useful defaults in performing-android-app-static-analysis-with-mobsf — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Pratham Ware· Dec 8, 2024

    performing-android-app-static-analysis-with-mobsf has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Chaitanya Patil· Dec 4, 2024

    I recommend performing-android-app-static-analysis-with-mobsf for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Hiroshi Zhang· Nov 27, 2024

    performing-android-app-static-analysis-with-mobsf reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Piyush G· Nov 23, 2024

    Useful defaults in performing-android-app-static-analysis-with-mobsf — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Aanya Zhang· Nov 3, 2024

    I recommend performing-android-app-static-analysis-with-mobsf for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Mateo Farah· Oct 22, 2024

    performing-android-app-static-analysis-with-mobsf reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Shikha Mishra· Oct 14, 2024

    performing-android-app-static-analysis-with-mobsf is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Harper Jackson· Sep 25, 2024

    performing-android-app-static-analysis-with-mobsf fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Nikhil Thompson· Aug 16, 2024

    Registry listing for performing-android-app-static-analysis-with-mobsf matched our evaluation — installs cleanly and behaves as described in the markdown.

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