# Siguza/ios-resources

Useful resources for iOS hacking

Repository: https://github.com/Siguza/ios-resources
Canonical: https://ross.abutalabs.com/products/ios-resources
License Family: other
Last push: 2025-05-24T22:38:16+00:00

## Health v2 (maintenance only)
Score: 43/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 23, release rhythm 35, longevity 100
- inputs: {"age_days": 3537, "days_push": 466, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1966, forks 270 (observed 2026-08-28T04:05:59.829672+00:00)

## What it is
A curated collection of resources for iOS hacking and reverse engineering, covering ARM64 assembly, Mach-O internals, sandbox, IPC, and related low-level topics. It is a documentation/awesome-list style repository of links and original write-ups rather than software.

## Use cases
- learn iOS reverse engineering
- find resources on Mach-O binary format
- study the Apple sandbox and entitlements
- learn arm64 assembly basics
- research iOS kernel and IPC internals
- prepare for iOS security research or jailbreak development

## When to choose
- you are starting or deepening iOS/ARM64 reverse engineering research
- you want a curated index of authoritative low-level Apple platform documents
- you need references on code signing, sandbox, or Mach IPC

## When to avoid
- you need runnable tools or code rather than reading material
- you target Android or other non-Apple platforms
- you want beginner-friendly app development tutorials

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, reverse-engineering, security
- domain: security, reverse-engineering, operating-systems, tutorials
- platform: -
- tags: awesome-list, ios-hacking, arm64, mach-o, jailbreak, curated-links, ios, macos

## Member repositories
- Siguza/ios-resources (main) score 43

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:59.829672+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:05:26.422547+00:00, confidence not recorded.
  - readme: https://github.com/Siguza/ios-resources (fetched 2026-08-28T04:05:59.829672+00:00, sha 510a7bf1e851)
- Data as of 2026-08-30T08:39:29.467469+00:00.