Agent skill

Detecting Mobile Malware Behavior

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation.

Apache-2.0Auto-check passedSecurity

Install Detecting Mobile Malware Behavior

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-mobile-malware-behavior -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-mobile-malware-behavior --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/detecting-mobile-malware-behavior .claude/skills/detecting-mobile-malware-behavior && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
detecting-mobile-malware-behavior
GitHub stars
34k
Token cost
~2.2k tokens
SKILL.md length
389 words
Files
8 (incl. scripts, references, assets)
Skills in repo
637
Repo updated
First seen
Licence
Apache-2.0

At a glance

Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation.

  • Works in 5 steps: Static Indicator Analysis → MobSF Automated Malware Scan → Network Behavior Monitoring → …
  • Analyzing suspicious mobile applications for data exfiltration
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 2 more sections
  • Runs Python scripts from its folder; calls curl and jq; reaches virustotal.com; needs VT_API_KEY and API_KEY

What it does

Detecting Mobile Malware Behavior is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when analyzing suspicious mobile applications for data exfiltration, command-and-control communication, credential stealing, SMS interception, or other malware indicators. Activates for requests involving mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts, reference files and assets (for example `assets/template.md`, `references/api-reference.md` and `references/standards.md`).

It sits in Security, covering Red teaming and adversary simulation and Reverse engineering and malware. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Analyzing suspicious mobile applications for data exfiltration
  • Command-and-control communication
  • Credential stealing
  • SMS interception

Example prompts

  • “Use the detecting-mobile-malware-behavior skill to detect and analyzes malicious behavior in mobile applications through behavioral analysis…”
  • “/detecting-mobile-malware-behavior”

Requirements

  • Python 3
  • A credential in VT_API_KEY
  • A credential in API_KEY

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Static Indicator Analysis
  2. MobSF Automated Malware Scan
  3. Network Behavior Monitoring
  4. Runtime Behavior Monitoring with Frida
  5. Classify Malware Type

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • curl
    • jq

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • virustotal.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • VT_API_KEY
    • API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Detecting Mobile Malware Behavior loads about 2.2k tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 130 tokens; SKILL.md has 389 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~130
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 389 words, ~2,192 tokens.

Download SKILL.mdSave it as .claude/skills/detecting-mobile-malware-behavior/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
detecting-mobile-malware-behavior
description
Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when analyzing suspicious mobile applications for data exfiltration, command-and-control communication, credential stealing, SMS interception, or other malware indicators. Activates for requests involving mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.
domain
cybersecurity
subdomain
mobile-security
author
mahipal
tags
mobile-security, android, ios, malware-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
mitre_attack
T1059, T1056, T1036, T1078, T1003
mitre_f3.version
1.1
mitre_f3.tactics
positioning, execution, initial-access

Detecting Mobile Malware Behavior

When to Use

Use this skill when:

  • Analyzing suspicious mobile applications submitted by users or discovered during incident response
  • Monitoring enterprise mobile fleet for malicious app indicators
  • Performing malware triage on APK/IPA samples
  • Investigating data exfiltration or unauthorized device access from mobile apps

Do not use this skill to create, enhance, or distribute malware. This skill is for defensive analysis only.

Prerequisites

  • Isolated analysis environment (dedicated device or emulator, not connected to production networks)
  • MobSF for automated static+dynamic analysis
  • Frida/Objection for runtime behavior monitoring
  • Wireshark/tcpdump for network traffic capture
  • Android emulator (AVD) or Genymotion for safe execution
  • VirusTotal API key for hash lookups

Workflow

Step 1: Static Indicator Analysis
bash
# Hash the sample
sha256sum suspicious.apk

# Check VirusTotal
curl -s "https://www.virustotal.com/api/v3/files/<SHA256>" \
  -H "x-apikey: <VT_API_KEY>" | jq '.data.attributes.last_analysis_stats'

# Extract permissions from AndroidManifest.xml
aapt dump permissions suspicious.apk

# High-risk permission combinations:
# READ_SMS + INTERNET = SMS stealer
# RECEIVE_SMS + SEND_SMS = SMS interceptor/banker trojan
# ACCESSIBILITY_SERVICE + INTERNET = overlay attack capability
# CAMERA + RECORD_AUDIO + INTERNET = spyware
# DEVICE_ADMIN + INTERNET = ransomware capability
# READ_CONTACTS + INTERNET = contact exfiltration
Step 2: MobSF Automated Malware Scan
bash
# Upload to MobSF
curl -F "file=@suspicious.apk" http://localhost:8000/api/v1/upload \
  -H "Authorization: <API_KEY>"

# Review malware indicators in report:
# - Hardcoded C2 server addresses
# - Dynamic code loading (DexClassLoader)
# - Reflection-based API calls (to evade static analysis)
# - Encrypted/obfuscated payloads
# - Root detection (malware often checks for root)
# - Anti-emulator checks (malware evades sandbox)
Step 3: Network Behavior Monitoring
bash
# Start packet capture on emulator
tcpdump -i any -w malware_traffic.pcap

# Or use mitmproxy for HTTP/HTTPS
mitmproxy --mode transparent

# Monitor for:
# - DNS lookups to suspicious/newly registered domains
# - Connections to known C2 infrastructure
# - Data exfiltration patterns (large POST requests)
# - Beaconing behavior (regular interval connections)
# - Non-standard ports and protocols
# - Domain Generation Algorithm (DGA) patterns
Step 4: Runtime Behavior Monitoring with Frida
javascript
// monitor_malware.js - Comprehensive behavior monitoring
Java.perform(function() {
    // Monitor SMS access
    var SmsManager = Java.use("android.telephony.SmsManager");
    SmsManager.sendTextMessage.overload("java.lang.String", "java.lang.String",
        "java.lang.String", "android.app.PendingIntent", "android.app.PendingIntent")
        .implementation = function(dest, sc, text, sent, delivery) {
            console.log("[SMS] Sending to: " + dest + " Text: " + text);
            // Allow or block based on analysis needs
            return this.sendTextMessage(dest, sc, text, sent, delivery);
        };

    // Monitor file operations
    var FileOutputStream = Java.use("java.io.FileOutputStream");
    FileOutputStream.$init.overload("java.lang.String").implementation = function(path) {
        console.log("[FILE-WRITE] " + path);
        return this.$init(path);
    };

    // Monitor network connections
    var URL = Java.use("java.net.URL");
    URL.openConnection.overload().implementation = function() {
        console.log("[NET] " + this.toString());
        return this.openConnection();
    };

    // Monitor dynamic code loading
    var DexClassLoader = Java.use("dalvik.system.DexClassLoader");
    DexClassLoader.$init.implementation = function(dexPath, optDir, libPath, parent) {
        console.log("[DEX-LOAD] Loading: " + dexPath);
        return this.$init(dexPath, optDir, libPath, parent);
    };

    // Monitor command execution
    var Runtime = Java.use("java.lang.Runtime");
    Runtime.exec.overload("java.lang.String").implementation = function(cmd) {
        console.log("[EXEC] " + cmd);
        return this.exec(cmd);
    };

    // Monitor camera/audio access
    var Camera = Java.use("android.hardware.Camera");
    Camera.open.overload("int").implementation = function(id) {
        console.log("[CAMERA] Camera opened: " + id);
        return this.open(id);
    };

    // Monitor content provider access (contacts, call log)
    var ContentResolver = Java.use("android.content.ContentResolver");
    ContentResolver.query.overload("android.net.Uri", "[Ljava.lang.String;",
        "java.lang.String", "[Ljava.lang.String;", "java.lang.String")
        .implementation = function(uri, proj, sel, selArgs, sort) {
            console.log("[QUERY] " + uri.toString());
            return this.query(uri, proj, sel, selArgs, sort);
        };

    console.log("[*] Malware behavior monitor active");
});
Step 5: Classify Malware Type

Based on observed behaviors, classify the sample:

Behavior PatternMalware Type
SMS interception + C2 communicationBanking Trojan
Camera/mic access + data uploadSpyware/Stalkerware
File encryption + ransom note displayMobile Ransomware
Ad injection + click fraud trafficAdware
Root exploit + persistenceRootkit
Contact harvesting + SMS spamWorm/SMS Spammer
Overlay attacks + credential captureCredential Stealer
Crypto mining network activityCryptojacker

Key Concepts

TermDefinition
Dynamic Code LoadingLoading executable code at runtime from external sources, commonly used by malware to evade static analysis
C2 BeaconRegular network check-in from malware to command-and-control server, identifiable by periodic timing patterns
DGADomain Generation Algorithm creating pseudo-random domain names for resilient C2 infrastructure
Overlay AttackDrawing fake UI over legitimate apps to capture credentials, requiring SYSTEM_ALERT_WINDOW permission
Anti-EmulatorTechniques malware uses to detect sandbox/emulator environments and suppress malicious behavior
Show full SKILL.md (121 more words)Show less

Tools & Systems

  • MobSF: Automated static and dynamic analysis for initial malware triage
  • VirusTotal: Multi-engine malware scanning and hash reputation lookup
  • Frida: Runtime behavior monitoring through method hooking
  • Wireshark: Network traffic analysis for C2 communication patterns
  • Cuckoo Sandbox / CuckooDroid: Automated malware analysis sandbox for Android samples

Common Pitfalls

  • Anti-analysis evasion: Sophisticated malware detects emulators, debuggers, and Frida. Use hardware devices and stealthy Frida configurations for accurate analysis.
  • Time-delayed payloads: Some malware activates only after a delay or specific trigger. Monitor for extended periods and simulate various conditions.
  • Encrypted C2: Malware using encrypted communications requires TLS interception or memory inspection to observe payload content.
  • Multi-stage payloads: Initial APK may be benign; malicious payload downloads later. Monitor for dynamic code loading and file downloads.

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references, assets) in skills/detecting-mobile-malware-behavior of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • assets/template.md
  • references/api-reference.md
  • references/standards.md
  • references/workflows.md
  • scripts/agent.py
  • scripts/process.py

Open the folder on GitHubat commit 54a7988

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Categories

Questions about Detecting Mobile Malware Behavior

What does Detecting Mobile Malware Behavior do?

Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Detecting Mobile Malware Behavior is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation.

When should I use Detecting Mobile Malware Behavior?

Detecting Mobile Malware Behavior fits situations like: analyzing suspicious mobile applications for data exfiltration; command-and-control communication; credential stealing; SMS interception.

How do I install Detecting Mobile Malware Behavior in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-mobile-malware-behavior -a claude-code`. Or copy the skill folder (skills/detecting-mobile-malware-behavior in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/detecting-mobile-malware-behavior in your project. Claude Code loads it when a task matches its description.

How do I install Detecting Mobile Malware Behavior in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-mobile-malware-behavior -a codex`. Or copy the skill folder (skills/detecting-mobile-malware-behavior in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/detecting-mobile-malware-behavior in your project. Codex loads it when a task matches its description.

Can I use Detecting Mobile Malware Behavior in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-mobile-malware-behavior -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/detecting-mobile-malware-behavior, .gemini/skills/detecting-mobile-malware-behavior, .github/skills/detecting-mobile-malware-behavior and .opencode/skills/detecting-mobile-malware-behavior in your project.

What does Detecting Mobile Malware Behavior need to run?

Going by SKILL.md and its folder, Detecting Mobile Malware Behavior needs Python for the scripts in its folder, the command-line tools its instructions call (curl and jq) and credentials named VT_API_KEY and API_KEY. Our summary lists: Python 3; A credential in VT_API_KEY; A credential in API_KEY.

Does Detecting Mobile Malware Behavior access the network?

SKILL.md names 1 domain. In commands or code: virustotal.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Detecting Mobile Malware Behavior safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Detecting Mobile Malware Behavior use?

Detecting Mobile Malware Behavior is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Detecting Mobile Malware Behavior use?

About 2.2k tokens (SKILL.md is roughly 8.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 934 tokens, read only when the agent opens those files.

What are the alternatives to Detecting Mobile Malware Behavior?

Skills that share tags, products or a category with Detecting Mobile Malware Behavior: Incident Response Network (LeoYeAI/openclaw-master-skills, 2.2k stars), vphone600 Kernel Symbol Analysis (Lakr233/vphone-cli, 15k stars), Webhome Extension Builder (webhtv/webhtv, 1.7k stars) and Reverse Flow (lingbol088-spec/reverse-flow-skill, 936 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Detecting Mobile Malware Behavior?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,922 GitHub stars. The repository holds 637 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.