Generalized from a long hardened-target engagement. Additions target the failure
modes that cost the most time and are not yet covered; nothing here is sample-specific.
New discipline
- SKILL.md: a third override rule -- deliver in the environment the request requires.
A root-only or live-instrumentation result can look like success while failing the
actual requirement (unrooted, installable, self-contained). Also adds two classify
questions: what form the deliverable must take, and which architecture/library is
actually executing.
- long-task-discipline.md: "the most expensive drift" -- a constraint table (privilege,
modification form, ABI, network, persistence, distribution), the checkpoint question,
and how to report a privileged workaround as a fallback rather than a deliverable.
Plus "bound every wait": timeouts on every command, budgets calibrated from
measurement so slow and hung are distinguishable, and sampling an observable you can
inspect instead of sleeping blind.
- environment.md: look at the screen instead of driving blind; which of the two screen
evidence kinds is trustworthy (an empty accessibility tree is common for
runtime-rendered UI, where the image is primary rather than a fallback); emulator
console control and adb recovery; preflight before every experiment block.
- native-and-so.md: what the device claims vs what the package manager chose vs what is
actually mapped; translation layers and their consequences for hooks and native
checks; fat-APK ABI selection.
- runtime-data.md: when the app rewrites your edit (server refresh vs local
regeneration), why chmod/chown cannot work because the app deletes and recreates the
file, and the immutable-attribute fix; decoding a value that only looks encrypted.
- framework-runtimes.md: string encoding traps (a UTF-8 scan of a UTF-16 snapshot
returns nothing and reads as "the strings were stripped"), and non-ASCII obfuscated
identifiers -- resolve by shape, not by name.
- pitfalls.md: P23 the fix lives in a file the app rewrites; P24 the artifact changed
but the wrong one is executing; P25 "search found nothing" treated as "not there".
New scripts (parameterized, path-agnostic, all with explicit timeouts)
- preflight.py environment check: device, root, ABI/translation, clock skew, leftover
proxy/forwards, dead device server, chosen ABI, free space
- lib_map.py what is actually mapped into a live process: path, base, architecture
from the ELF header, and whether it came from the APK or was
materialized at runtime
- blob_decode.py search (don't guess) the framing of a stored value, then re-encode the
edited payload with the same parameters
- snap.py bounded burst screenshots + control-tree capture, with a stall detector
and an explicit verdict on whether the tree is usable
All four were exercised against a live device while writing: preflight correctly
flags multi-device ambiguity and detected the ARM translation layer; lib_map read real
ELF headers and identified the translation shim; blob_decode round-tripped a
rotation+deflate+base64 blob at realistic size in ~0.2s; snap detected a stalled screen
across three hashes.
137 lines
7.9 KiB
Markdown
137 lines
7.9 KiB
Markdown
# apk-reverse
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An Agent Skill for Android APK reverse engineering, debloating, ad removal, surgical
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dex patching, repacking, and runtime/server analysis.
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It is a **skill**, not a tutorial: it is written to be loaded by an agent (Claude Code,
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Codex, or any harness that supports the Agent Skills format) while it works, so it is
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organized for progressive disclosure — a short decision-oriented `SKILL.md`, detailed
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references loaded only when a step needs them, and parameterized scripts you can run
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directly.
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## What it is good at
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- Deciding **fast** whether a request is even achievable client-side, instead of
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burning hours on a paywall that is enforced by a server.
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- Deciding **what form the deliverable must take** before any work starts — an
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unrooted, self-contained artifact is a different problem from "make it work on this
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machine", and confusing the two is the most expensive drift in this domain.
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- Choosing the **safest patch layer** for a given change, and avoiding the layers that
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break the app.
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- Separating **your own mistakes from the app's or the server's problems** — a
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feature-scoped failure (login, registration, payment) is often a TLS/certificate issue on
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one code path, not a consequence of the patch you just built. Device state, a dead device
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server and clock drift masquerade the same way.
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- Establishing **which architecture and which library are actually executing**, rather
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than trusting what the manifest ships or what the device claims.
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- Working through **packed/hardened targets**: identifying the packer, unpacking, and turning a
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memory dump back into a patched, installable APK.
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- Keeping a **long task honest**: a live record, graded conclusions, calibrated timeouts, and
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bounded waits, so progress is not lost and the same mistake is not made twice.
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- Avoiding the specific mistakes that produce an APK that builds perfectly and dies at
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runtime.
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## Structure
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```
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SKILL.md decision tree, workflow, hard constraints, indexes
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references/ loaded on demand, one topic each
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recon.md identify packer, SDKs, code location, tamper checks; unpacking
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packers.md hardened targets: rejection signals, measuring the validation
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boundary with single-variable tests, choosing a native host
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framework-runtimes.md Flutter / React Native / Unity: which layer owns the UI, and how to
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find logic when there are no symbols (string encoding traps)
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native-and-so.md .so hosts, DT_NEEDED vs JNI_OnLoad, relocation limits,
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relocation-free bootstrapping, replacing Java methods natively,
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and which ABI/library is *actually loaded and executing*
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long-task-discipline.md live record, conclusion grading, drift control, timeout and
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wait calibration, deliverable-form drift, handover
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ad-removal.md ad taxonomy, wrapper mapping, callback trap, global gates, verification
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membership-and-limits.md server vs client authority; what is and is not patchable
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server-api.md probe an app's API; prove who owns the gate
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tls-and-cert.md feature-scoped network failures: expired certs, dual trust chains
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third-party-builds.md auditing a "cracked"/"modded" APK before trusting it
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dex-patching.md patch-layer table + dexlib2 technique in depth
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repack-and-sign.md repack rules, unpack-and-repack, signing, post-install hazards
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runtime-data.md DataStore / SharedPreferences / SQLite / protobuf; when the app
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rewrites your edit, and decoding a value that looks encrypted
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dynamic-frida.md Frida setup, version pinning, the four-layer probe, hook strategy
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environment.md device/emulator setup, root, ADB, offline devices, log signals,
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emulator console control and recovery, preflight, look-at-the-screen
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verification.md the claim ladder; what "done" means
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pitfalls.md the failure catalogue -- read before building
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scripts/ parameterized, path-agnostic
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smtool.py baksmali/smali wrapper with a configurable classpath
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dexpatch/ dexlib2 method-level surgical rewriter (preferred tool)
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patch_smali.py method-body replacement in a smali tree
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dex_strpatch.py byte-level string patch with a string_ids ordering guard
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dex_classdiff.py prove a dex edit was surgical
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dex_strings.py strings/URLs/SDK markers without a decompiler
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find_refs.py count callers of a method before patching it
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repack.py rebuild APK, strip only signatures, sign, verify
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devsh.py quoting-safe ADB shell helper
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usb_net_proxy.py give an offline device network over USB
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datastore_inject.py encode/inject AndroidX DataStore preferences safely
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probe_api.py probe an HTTP API with the right headers
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grab_crash.py recover stacks hidden by a crash-reporter SDK
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install_test.py install + launch health check with logcat signal scan
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frida_probe.js four-layer runtime probe (app net layer + OkHttp + java.net + exceptions)
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run_probe.py inject the probe, stream it to a log file, stay resident
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tls_check.py strict certificate check for one or more hosts
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preflight.py environment check before every experiment block (device, root,
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ABI/translation, clock skew, leftover proxy/forwards, dead server)
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lib_map.py what is *actually mapped* into a live process: per-library path,
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base, architecture, and whether it came from the APK or was
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materialized at runtime
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blob_decode.py search, don't guess, the framing of a stored value
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(base64/hex x rotation x deflate); re-encode the edited payload
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snap.py bounded burst screenshots + control-tree capture with a stall
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detector, and a verdict on whether the tree is usable at all
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```
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## Install
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Drop this directory where your agent finds skills, e.g.:
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```
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<skills-dir>/apk-reverse/SKILL.md
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```
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The agent loads `SKILL.md` when a task matches its description, and pulls in
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`references/*` only as needed. No global state, no machine-specific paths.
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## Requirements
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Nothing is mandatory; each script checks what it needs.
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| Tool | Used for |
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|---|---|
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| Python 3.9+ | all scripts |
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| `baksmali` / `smali` + `dexlib2` jars | disassembly, assembly, surgical patching |
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| JDK (`javac`, `java`) | building/running the dexlib2 patcher |
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| Android SDK build-tools (`aapt`, `zipalign`, `apksigner`) | manifest info, alignment, signing |
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| `uber-apk-signer` (optional) | one-step align + sign |
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| ADB | device work |
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| Frida (host package + matching on-device server) | dynamic analysis |
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| a rooted device or emulator | anything beyond static analysis |
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## Read this first
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`references/pitfalls.md`. It is the most valuable file here — every entry is a failure
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that produced a broken artifact while looking completely healthy.
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The three that hurt most:
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1. Stripping the whole `META-INF/` during a repack deletes ServiceLoader registrations
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and the app dies at startup with an error that names an unrelated library.
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2. Patching a byte-level string without preserving `string_ids` ordering gets the whole
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dex rejected, while checksums and signatures verify perfectly.
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3. Rebuilding a dex with a whole-tree smali round-trip damages R8 output invisibly —
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class tables compare clean, and it only blows up at runtime.
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## Scope
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Built for working on your own applications, on samples you are authorized to analyze,
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and in CTF/competition sandboxes. It contains no vendored third-party binaries and no
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target-specific data.
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