Mobile Security logo

Mobile Security

Organization
transilienceai
mobile-security

Mobile application security testing (Android + iOS) mapped to OWASP MASVS/MASTG — static reversing (Flutter AOT, Unity IL2CPP, React Native/Hermes, native ARM64, Mach-O/Swift), SAST (manifest/IPC, storage, crypto, signing), dynamic analysis (Frida/objection, TLS-pinning + root/jailbreak bypass, traffic interception), and privacy testing.

Overview

Publishertransilienceai
Repositorycommunitytools
Skill namemobile-security
Stars
565
Forks
81
Bundled files
10
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by transilienceai on GitHub. Read the source before you install it.

Installation

Install the Mobile Security AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/transilienceai/communitytools/tree/main/skills/mobile-security
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/transilienceai/communitytools.git /tmp/communitytools
mkdir -p .claude/skills
cp -r /tmp/communitytools/skills/mobile-security .claude/skills/mobile-security
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mobile Security in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mobile Security on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mobile Security is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Mobile Security

Scope

End-to-end mobile application VAPT for Android (APK/AAB) and iOS (IPA), aligned to the OWASP MASVS v2.x control groups and the MASTG testing process. Four complementary tiers:

  1. Static reverse engineering of compiled artifacts — Dart AOT snapshots, Unity IL2CPP, React Native/Hermes bytecode, native ARM64 .so/Mach-O, smali. Recover secrets, endpoints, and the crypto contract without a device.
  2. SAST — manifest / Info.plist, exported-component & IPC surface, WebView, local storage, cryptographic-primitive weakness, code-signing, and automated baseline (MobSF/apkid/apkleaks) → then manual deep-dive.
  3. Dynamic analysis (DAST) — Frida/objection instrumentation, traffic interception, TLS-pinning bypass across stacks, Keychain/Keystore runtime dumps, IPC probing, and root/jailbreak/anti-tamper defeat for MAS-L2 / MASA scope.
  4. Privacy — data-collection inventory, tracker/SDK enumeration, PII leakage, declared-vs-actual (Play Data Safety / Apple Privacy Manifest).

Static dump first (faster, no device); dynamic is a first-class phase whenever a control can only be proven at runtime (enforced pinning, Keystore-backed keys, root reaction, IPC guards). Cross-asset stitching, scoring, and reporting are owned by sibling skills — this skill produces MASVS/MASTG-tagged findings and hands them off.

Coverage contract

In a coverage-mode engagement (pentest-engagement mobile mode) completion is code-enforced, not narrative. Two surfaces are gated, and both are mandatory:

SurfaceFileClasses
The app bundlerecon/inventory/mobile-surface.jsonthe 15 MAS-* MASVS classes
The backend recovered from the bundle<apex>-api/recon/inventory/surface.jsonthe ordinary OWASP API/web classes

The second row is where the material risk has historically been. A decompiled bundle hands you the full server contract, and that surface is not browser-reachable — so it is systematically under-tested by everyone, including the app's own developers. Recovering the endpoint inventory and driving it through the API classes is not an optional extra; a bundle that yields zero endpoints is treated as a failed acquisition.

Per-class detail: reference/masvs-class-map.md. Two rules worth internalising before you write a negative:

  • proof_mode: runtime cannot be closed statically. Pinning and root detection are the classic traps: static analysis can prove a control is inert (a CertificatePinner built and never attached, a RootBeer that no DEX references) — raise that as a positive. It can never prove the control is effective; that needs a bypass attempt that failed, and a failed bypass is a legitimate, reportable result.
  • proof_mode: static can never be device-deferred. No device does not excuse the manifest, the signature, the bundled dependencies, or the secrets in the artifact.

When to use

  • Target ships an Android APK/AAB or iOS IPA — extract and inspect before any runtime testing.
  • Built with Flutter (lib/arm64-v8a/libapp.so / iOS App.framework), Unity (libil2cpp.so + global-metadata.dat), or React Native + Hermes (libhermes.so + index.android.bundle) — needs a runtime-aware decompiler, not just jadx.
  • Stock Android/iOS app — you need the manifest/IPC/storage/crypto/signing attack surface (SAST) and, where a control is runtime-only, dynamic confirmation.
  • App uses encrypted API envelopes (KEY/IV/SALT/SIGNATURE headers) and you need to reverse the crypto contract, then replay against the live API.
  • TLS pinning / root / jailbreak detection blocks testing — bypass it dynamically (or defeat it statically) and demonstrate the protected flow.
  • You suspect IDOR / mass assignment / business-logic flaws easier to find in the dumped client, then confirmed server-side.
  • You need a MASVS-PRIVACY pass (trackers, over-collection, PII leakage, declared-vs-actual).

Methodology

Start at reference/methodology.md — the phase backbone (ACQUIRE → TRIAGE → STATIC → DYNAMIC → NETWORK → STORAGE → PLATFORM/IPC → BACKEND PIVOT → REPORT), app acquisition + evidence integrity, the MASVS→file coverage map, finding-tagging convention, and the client→API pivot. It routes to every reference below. Do preflight (../coordination/reference/preflight-checklist.md) first.

References

Cross-cutting

Android

  • reference/android-static-analysis.md — SAST: MobSF/apkid/apkleaks baseline, manifest & exported-component/IPC, ContentProvider SQLi/traversal, PendingIntent, deep links, native WebView RCE, storage & Keystore review, crypto-primitive weakness pass, NSC, apksigner/Janus, SBOM.
  • reference/android-dynamic-analysis.md — DAST: device/Magisk/Zygisk setup, frida-server bring-up, objection recipes, interception + Android-7 user-CA workarounds, cross-stack pinning bypass (OkHttp/BoringSSL-Flutter/RN), drozer IPC, runtime storage, RESILIENCE active bypass + repack/resign.

iOS

  • reference/ios-static-analysis.md — SAST: IPA acquisition + FairPlay decrypt (cryptid), Mach-O/ObjC/Swift RE, ATS, entitlements/provisioning, binary hardening, Keychain accessibility + Data Protection, URL schemes/Universal Links, WKWebView, pasteboard/snapshot, MobSF/SBOM.
  • reference/ios-dynamic-analysis.md — DAST: jailbroken vs non-JB (objection patchipa / frida-gadget) bring-up, objection/Frida on iOS, SSL Kill Switch / SecTrust pinning bypass, Keychain dump, LAContext biometric bypass, jailbreak/anti-debug defeat, method tracing.

Framework-specific reverse engineering

Cross-skill (reused capabilities — cross-linked, not duplicated)

Deterministic control-wiring detector

  • ../../tools/apk_control_wiring.py — static cross-reference over a decompiled Android tree that distinguishes a REAL applied control from an ORPHANED one: RootBeer/SafetyNet/Play-Integrity shipped-but-unwired (referenced but the result gates nothing), CertificatePinner built-but-not-attached to an OkHttpClient, hardcoded AES/DES key literals + their invoke-sites, and bundled-but-never-loaded .so. Run it in the STATIC phase BEFORE authoring remediation verdicts — a naive re-test that only greps for the control's presence wrongly reports an inert control "fixed" (a recurring mobile re-test crux).

Anti-patterns

  • Reaching for Frida/emulator before the static dump exists — static-first is faster and needs no device. But do not treat dynamic as out of scope: pinning enforcement, Keystore binding, root reaction, and IPC reachability are runtime-only.
  • Reporting a control as present (root/pinning/tamper detection) without an active bypass attempt — MAS-L2 / MASA require you defeat it or prove you can't.
  • Concluding "no pinning" from an empty network_security_config.xml, or "no secrets" from a cryptid 1 iOS binary — check the JS/native pin layers, and decrypt the IPA first.
  • Stopping at a client-side IDOR — it is a server-side hypothesis; confirm via the api-security pivot.
  • Reporting a CVE from a library's mere presence — confirm the exact version and a reachable code path.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mobile Security AI skill do?

Mobile application security testing (Android + iOS) mapped to OWASP MASVS/MASTG — static reversing (Flutter AOT, Unity IL2CPP, React Native/Hermes, native ARM64, Mach-O/Swift), SAST (manifest/IPC, storage, crypto, signing), dynamic analysis (Frida/objection, TLS-pinning + root/jailbreak bypass, traffic interception), and privacy testing.

Why use Mobile Security on TypingMind?

Because you install it once and use it with any model. Mobile Security is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mobile Security in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/transilienceai/communitytools/tree/main/skills/mobile-security. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mobile Security?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mobile Security?

As many as you like. As long as a model supports skills, you can use Mobile Security with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mobile Security AI skill free?

Yes. It is published on GitHub by transilienceai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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