performing-dynamic-analysis-of-android-app▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Performs runtime dynamic analysis of Android applications using Frida, Objection, and Android Debug Bridge to observe application behavior during execution, intercept function calls, modify runtime values, and identify vulnerabilities that static analysis misses. Use when testing Android apps for runtime security flaws, hooking sensitive methods, bypassing client-side protections, or analyzing obfuscated applications. Activates for requests involving Android dynamic analysis, runtime hooking, Frida Android instrumentation, or live app behavior analysis.
| name | performing-dynamic-analysis-of-android-app |
| description | 'Performs runtime dynamic analysis of Android applications using Frida, Objection, and Android Debug Bridge to observe application behavior during execution, intercept function calls, modify runtime values, and identify vulnerabilities that static analysis misses. Use when testing Android apps for runtime security flaws, hooking sensitive methods, bypassing client-side protections, or analyzing obfuscated applications. Activates for requests involving Android dynamic analysis, runtime hooking, Frida Android instrumentation, or live app behavior analysis. ' |
| domain | cybersecurity |
| subdomain | mobile-security |
| author | mahipal |
| tags | - mobile-security - android - frida - dynamic-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 Dynamic Analysis of Android App
When to Use
Use this skill when:
- Static analysis results need runtime validation on an actual Android device
- The target app uses obfuscation (DexGuard, custom packers) that prevents effective static analysis
- Testing requires observing actual API calls, decrypted data, or runtime-generated values
- Assessing root detection, tamper detection, or anti-debugging implementations
Do not use this skill on production environments without authorization -- dynamic instrumentation can alter app behavior and trigger security alerts.
Prerequisites
- Rooted Android device or emulator (Genymotion, Android Studio AVD with writable system)
- Frida server installed on device matching the architecture (arm64, x86_64)
- Python 3.10+ with
frida-toolsandobjectionpackages - ADB configured and device connected
- Target APK installed on device
Workflow
Step 1: Setup Frida Server on Android Device
# Check device architecture
adb shell getprop ro.product.cpu.abi
# Output: arm64-v8a
# Download matching Frida server from GitHub releases
# https://github.com/frida/frida/releases
# Push to device
adb push frida-server-16.x.x-android-arm64 /data/local/tmp/frida-server
adb shell chmod 755 /data/local/tmp/frida-server
adb shell /data/local/tmp/frida-server &
# Verify Frida connection
frida-ps -U
Step 2: Enumerate Application Attack Surface
# List all packages
frida-ps -U -a
# Attach Objection for high-level exploration
objection --gadget com.target.app explore
# List activities, services, receivers
android hooking list activities
android hooking list services
android hooking list receivers
# List loaded classes
android hooking list classes
android hooking search classes com.target.app
Step 3: Hook Sensitive Methods
# Hook all methods of a class
android hooking watch class com.target.app.auth.LoginManager
# Hook specific method with argument dumping
android hooking watch class_method com.target.app.auth.LoginManager.authenticate --dump-args --dump-return
# Hook crypto operations
android hooking watch class javax.crypto.Cipher --dump-args
android hooking watch class java.security.MessageDigest --dump-args
# Hook network calls
android hooking watch class okhttp3.OkHttpClient --dump-args
android hooking watch class java.net.URL --dump-args
Step 4: Write Custom Frida Scripts for Deep Analysis
// hook_crypto.js - Intercept encryption/decryption operations
Java.perform(function() {
var Cipher = Java.use("javax.crypto.Cipher");
Cipher.doFinal.overload("[B").implementation = function(input) {
var mode = this.getAlgorithm();
console.log("[Cipher] Algorithm: " + mode);
console.log("[Cipher] Input: " + bytesToHex(input));
var result = this.doFinal(input);
console.log("[Cipher] Output: " + bytesToHex(result));
return result;
};
function bytesToHex(bytes) {
var hex = [];
for (var i = 0; i < bytes.length; i++) {
hex.push(("0" + (bytes[i] & 0xFF).toString(16)).slice(-2));
}
return hex.join("");
}
});
# Execute custom Frida script
frida -U -f com.target.app -l hook_crypto.js --no-pause
Step 5: Bypass Root Detection
// root_bypass.js - Common root detection bypass
Java.perform(function() {
// Bypass RootBeer library
var RootBeer = Java.use("com.scottyab.rootbeer.RootBeer");
RootBeer.isRooted.implementation = function() {
console.log("[RootBeer] isRooted() bypassed");
return false;
};
// Bypass generic file-based root checks
var File = Java.use("java.io.File");
var originalExists = File.exists;
File.exists.implementation = function() {
var path = this.getAbsolutePath();
var rootPaths = ["/system/app/Superuser.apk", "/system/xbin/su",
"/sbin/su", "/system/bin/su", "/data/local/bin/su"];
if (rootPaths.indexOf(path) >= 0) {
console.log("[Root] Blocked check for: " + path);
return false;
}
return originalExists.call(this);
};
// Bypass SafetyNet/Play Integrity
try {
var SafetyNet = Java.use("com.google.android.gms.safetynet.SafetyNetApi");
console.log("[SafetyNet] Class found - may need additional bypass");
} catch(e) {}
});
Step 6: Analyze Network Communication at Runtime
// network_monitor.js - Monitor all HTTP requests
Java.perform(function() {
// Hook OkHttp3
try {
var OkHttpClient = Java.use("okhttp3.OkHttpClient");
var Interceptor = Java.use("okhttp3.Interceptor");
var Chain = Java.use("okhttp3.Interceptor$Chain");
console.log("[OkHttp] Monitoring network requests...");
var Request = Java.use("okhttp3.Request");
Request.url.implementation = function() {
var url = this.url();
console.log("[OkHttp] URL: " + url.toString());
return url;
};
} catch(e) {
console.log("[OkHttp] Not found, trying HttpURLConnection");
}
// Hook HttpURLConnection
var URL = Java.use("java.net.URL");
URL.openConnection.overload().implementation = function() {
console.log("[URL] Opening: " + this.toString());
return this.openConnection();
};
});
Step 7: Extract Decrypted Data and Secrets
# Using Objection for quick extraction
objection --gadget com.target.app explore
# Dump Android Keystore entries
android keystore list
android keystore dump
# Search heap for sensitive objects
android heap search instances com.target.app.model.User
android heap evaluate <handle> "JSON.stringify(clazz)"
# Memory string search
memory search "password" --string
memory search "api_key" --string
Key Concepts
| Term | Definition |
|---|---|
| Dynamic Instrumentation | Modifying application behavior at runtime by injecting code into the running process |
| Method Hooking | Replacing or wrapping function implementations to intercept arguments and return values |
| Frida Server | Daemon running on the target device that receives instrumentation commands from the host |
| Dalvik/ART Runtime | Android runtime environments; Frida hooks at the ART level for Java/Kotlin methods |
| Heap Inspection | Examining live objects in the application's memory heap to extract runtime data |
Tools & Systems
- Frida: Dynamic instrumentation toolkit for injecting JavaScript into native Android processes
- Objection: Higher-level Frida wrapper with pre-built Android and iOS security testing commands
- frida-trace: Automated method tracing utility for quick reconnaissance of app behavior
- Drozer: Android security assessment framework for testing IPC and exported components
- Android Studio Profiler: Runtime monitoring for CPU, memory, and network activity
Common Pitfalls
- Frida version mismatch: The Frida server on the device must match the frida-tools version on the host. Version mismatches cause connection failures.
- Anti-Frida detection: Some apps detect Frida by checking for the Frida server process, scanning memory for Frida signatures, or monitoring
/proc/self/maps. Use Frida Gadget injection or custom server builds. - Obfuscated class names: When ProGuard/R8 is applied, class and method names are shortened (e.g.,
a.b.c.d()). Useandroid hooking search classesto discover actual runtime names. - Multi-DEX apps: Large apps split across multiple DEX files may not have all classes loaded at startup. Hook class loaders or use
Java.enumerateLoadedClasses()after app is fully initialized.
How to use performing-dynamic-analysis-of-android-app on Cursor
AI-first code editor with Composer
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-dynamic-analysis-of-android-app
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches performing-dynamic-analysis-of-android-app from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate performing-dynamic-analysis-of-android-app. Access the skill through slash commands (e.g., /performing-dynamic-analysis-of-android-app) 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.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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.Install data analysis skill using provided command
- 2.Prepare a sample dataset (CSV, JSON, or database connection)
- 3.Start with descriptive statistics: 'Summarize this dataset'
- 4.Progress to visualization: 'Create a scatter plot of X vs Y'
- 5.Advanced analysis: 'Run linear regression and interpret results'
- 6.Validate outputs: check calculations, verify visualizations make sense
- 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▌
- 1Basic: descriptive statistics, data cleaning, simple visualizations
- 2Intermediate: hypothesis testing, regression, correlation analysis
- 3Advanced: time series analysis, clustering, predictive modeling
- 4Expert: causal inference, experimental design, advanced statistical methods
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★55 reviews- ★★★★★Lucas Garcia· Dec 28, 2024
Solid pick for teams standardizing on skills: performing-dynamic-analysis-of-android-app is focused, and the summary matches what you get after install.
- ★★★★★Zara Jackson· Dec 24, 2024
We added performing-dynamic-analysis-of-android-app from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Pratham Ware· Dec 16, 2024
Registry listing for performing-dynamic-analysis-of-android-app matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Dev Park· Dec 4, 2024
Keeps context tight: performing-dynamic-analysis-of-android-app is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Diego Malhotra· Dec 4, 2024
performing-dynamic-analysis-of-android-app reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Omar Martinez· Nov 27, 2024
performing-dynamic-analysis-of-android-app is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Isabella Chen· Nov 23, 2024
I recommend performing-dynamic-analysis-of-android-app for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Diego Sethi· Nov 23, 2024
Registry listing for performing-dynamic-analysis-of-android-app matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Dev Kim· Nov 19, 2024
performing-dynamic-analysis-of-android-app has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Mateo Ndlovu· Nov 15, 2024
Useful defaults in performing-dynamic-analysis-of-android-app — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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