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Analyzing Macos Binaries

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trilwu
analyzing-macos-binaries

Reverse engineer and security-review macOS applications and Mach-O binaries — thinning universal binaries, recovering Objective-C/Swift structure, reading code-signing entitlements and the hardened runtime, and auditing XPC services, dylib load paths, and TCC privacy exposure. Use when analyzing a .app bundle or Mach-O on macOS, checking entitlements and notarization, hunting a dylib-hijack or XPC privilege bug, or reasoning about Gatekeeper and quarantine.

Overview

Publishertrilwu
Repositorysecskills
Skill nameanalyzing-macos-binaries
Stars
144
Forks
15
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Analyzing Macos Binaries 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.
  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/trilwu/secskills.git /tmp/secskills
mkdir -p .claude/skills
cp -r /tmp/secskills/secskills-core/skills/analyzing-macos-binaries .claude/skills/analyzing-macos-binaries
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyzing Macos Binaries 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 Analyzing Macos Binaries 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 Analyzing Macos Binaries 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.

Analyzing macOS Binaries

macOS reverse engineering is Mach-O plus a specific security model: code signing, entitlements, the sandbox, TCC privacy, and XPC between processes. The binary tells you what the code does; the entitlements and load paths tell you what it is allowed to do and where an attacker could get in. Read both — most macOS findings live in the gap between the two.

When to Use

  • Analyzing a .app bundle or a Mach-O executable/dylib/framework on macOS
  • Reading entitlements, hardened-runtime flags, notarization, and Gatekeeper quarantine state
  • Auditing an XPC service or privileged helper for an authorization bug
  • Hunting dylib hijacking / proxying via @rpath and weak dylibs
  • Reasoning about TCC privacy exposure and sandbox escape surface

When NOT to Use

  • iOS apps — IPA decryption, FairPlay (cryptid=1), App Store binaries — are analyzing-ios-binaries. Same Mach-O format, different toolchain and DRM.
  • Non-Mach-O or cross-platform triage with no macOS specifics — analyzing-binaries.
  • Turning a memory-corruption crash into an exploitexploiting-memory-corruption.
  • A Mach-O that is packed/encrypted before you can read itunpacking-protected-binaries.

Triage the Binary and Bundle

  • Thin the universal binary. macOS ships fat binaries (x86_64 + arm64); use lipo -thin (or lipo -archs to list) so your tools work on one slice.
  • Read the Mach-O. otool -l for load commands, otool -L for linked dylibs and their paths, nm for symbols. Note the load commands that matter later: LC_RPATH, LC_LOAD_DYLIB / LC_LOAD_WEAK_DYLIB, and the code signature.
  • Walk the bundle. Contents/MacOS (the binary), Info.plist (identifiers, URL schemes), _CodeSignature, embedded Frameworks/, and any bundled XPCServices/. The bundle layout is the map of what to analyze.

Recover Objective-C and Swift

  • Objective-C keeps rich runtime metadata: class-dump (or dsdump) reconstructs class, method, and property declarations straight from the binary. This is the fastest way to see the app's structure.
  • Swift is harder — names are mangled and metadata is less forthcoming. Run swift-demangle over symbols to get readable names, and expect to lean on the decompiler (Hopper, Ghidra, IDA) more than with Objective-C.
  • Note which language dominates before choosing the approach; a Swift binary where class-dump returns little is normal, not a failure.

Code Signing, Entitlements, and Gatekeeper

This is where macOS-specific authority lives:

  • codesign -dvvv --entitlements :- <binary> dumps the signature and the entitlements — the capabilities the OS grants. Entitlements like com.apple.security.get-task-allow (debuggable), disabled library validation, or private TCC entitlements are the high-value reads.
  • Hardened runtime flags restrict code injection and debugging; note whether they are on, and whether library validation is disabled (which allows loading unsigned dylibs — directly relevant to hijacking).
  • Notarization and Gatekeeper. Downloaded files carry the com.apple.quarantine extended attribute; Gatekeeper checks notarization on first run. Understand the quarantine/notarization state when reasoning about what will execute and what a user was warned about.

The macOS-Specific Bug Surface

  • Dylib hijacking / proxying. A binary that loads a dylib from an @rpath that resolves to a writable location, or a LC_LOAD_WEAK_DYLIB that is absent, lets an attacker drop a malicious dylib and get code execution in the app's context — inheriting its entitlements. Enumerate the load paths and check which are attacker-writable and unprotected by library validation.
  • XPC services and privileged helpers. XPC is the mach-based IPC between an app and its helpers (often a SMJobBless root helper). The classic bug is a helper that authorizes a client by PID — which races and is spoofable — instead of by audit_token. Trace how the service validates its caller and what privileged action it will perform; weak validation is local privilege escalation.
  • TCC privacy. TCC gates access to camera, mic, files, and automation. Look at what the app is entitled to and whether it can be coerced into acting as a confused deputy for a less-privileged process, or whether an injectable dylib inherits its TCC grants.
  • URL schemes and Info.plist handlers register the app to handle input from other apps and the web — an untrusted-input entry point.

Dynamic Analysis

Frida attaches to macOS processes for runtime hooking; lldb debugs (subject to get-task-allow/SIP); dtrace traces syscalls and library calls where SIP permits. Use these to watch XPC messages and dylib loads live rather than inferring them from the binary alone.

Rationalizations to Reject

  • "It's signed and notarized, so it's safe." Signing proves origin, not safety, and says nothing about a dylib-hijack path or an XPC helper that trusts its caller's PID. Read the entitlements and load paths.
  • "class-dump returned almost nothing, the binary is stripped." That is the normal signature of a Swift binary. Switch to swift-demangle and the decompiler rather than concluding there is nothing to see.
  • "The helper checks the client PID, that's authentication." PID checks race and are spoofable. Only audit_token-based validation is sound; a PID check is the finding.
  • "Library validation will stop a malicious dylib." Only if it is enabled. Check for the disable-library-validation entitlement and hardened-runtime state before assuming the load is protected.
  • "This is just the iOS process on a Mac." The DRM, toolchain, and security model differ. Use analyzing-ios-binaries for FairPlay IPAs; this skill for the macOS app and its XPC/TCC/dylib surface.

References

  • analyzing-ios-binaries — iOS IPAs, FairPlay, and mobile toolchain
  • analyzing-binaries — general Mach-O triage and decompilation technique
  • exploiting-memory-corruption — exploiting a native bug found here
  • unpacking-protected-binaries — when the Mach-O is packed/encrypted first

Frequently asked questions

What does the Analyzing Macos Binaries AI skill do?

Reverse engineer and security-review macOS applications and Mach-O binaries — thinning universal binaries, recovering Objective-C/Swift structure, reading code-signing entitlements and the hardened runtime, and auditing XPC services, dylib load paths, and TCC privacy exposure. Use when analyzing a .app bundle or Mach-O on macOS, checking entitlements and notarization, hunting a dylib-hijack or XPC privilege bug, or reasoning about Gatekeeper and quarantine.

Why use Analyzing Macos Binaries on TypingMind?

Because you install it once and use it with any model. Analyzing Macos Binaries 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 Analyzing Macos Binaries in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trilwu/secskills/tree/main/secskills-core/skills/analyzing-macos-binaries. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Analyzing Macos Binaries?

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 Analyzing Macos Binaries?

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

Is the Analyzing Macos Binaries AI skill free?

Yes. It is published on GitHub by trilwu 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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