# bkerler/exploit_me

Very vulnerable ARM/AARCH64 application (CTF style exploitation tutorial with 29 vulnerability techniques)

Repository: https://github.com/bkerler/exploit_me
Canonical: https://ross.abutalabs.com/products/exploit_me
Language: C++
License: MIT
License Family: permissive
Topics: exploitation, tutorial, ctf, arm, rop, cybersecurity-education, educational, vulnerabilities
Last push: 2026-06-28T12:34:49+00:00

## Health v2 (maintenance only)
Score: 80/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 89, release rhythm 56, longevity 100
- inputs: {"age_days": 3165, "days_push": 66, "days_rel": 83, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1106, forks 158 (observed 2026-08-28T04:03:36.557356+00:00)

## What it is
A deliberately vulnerable ARM/ARM64 C++ application offering 29 CTF-style exploitation levels covering techniques like stack overflow, format string, ROP, and use-after-free. It is a hands-on training resource for learning binary exploitation with GDB and pwndbg.

## Use cases
- learn binary exploitation on ARM
- practice ROP chain building
- CTF-style exploitation training
- study format string vulnerabilities
- practice heap exploitation techniques
- learn to debug ARM binaries with gdb

## When to choose
- you want hands-on practice exploiting ARM/ARM64 binaries
- you are preparing for CTF binary exploitation challenges
- you want guided levels covering many vulnerability classes

## When to avoid
- you need a security scanning or defensive tool
- you only target x86 exploitation without porting
- you want production-ready software

## Facets
- artifact type: learning-resource
- maturity: active
- function: security, developer-tools
- domain: security, education, tutorials
- platform: cross-platform
- tags: ctf, exploitation, arm, aarch64, rop, binary-exploitation, vulnerability-training, hands-on, linux

## Member repositories
- bkerler/exploit_me (main) score 80

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:36.557356+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 2026-08-30T06:44:13.688464+00:00, confidence not recorded.
  - readme: https://github.com/bkerler/exploit_me (fetched 2026-08-28T04:03:36.557356+00:00, sha 26c44cd9c49f)
- Data as of 2026-08-30T08:39:29.467469+00:00.