# jakespringer/angr_ctf

Repository: https://github.com/jakespringer/angr_ctf
Canonical: https://ross.abutalabs.com/products/angr_ctf
Language: Python
License: GPL-3.0
License Family: copyleft
Last push: 2024-03-14T22:36:17+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3346, "days_push": 902, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1123, forks 176 (observed 2026-08-28T04:03:40.589289+00:00)

## What it is
A collection of capture-the-flag style challenges designed to teach binary analysis and symbolic execution with the angr framework. It includes a build system that compiles per-user challenge binaries for local play or MetaCTF web deployment.

## Use cases
- learn angr symbolic execution from scratch
- practice reverse engineering ctf challenges
- find passwords in binaries with symbolic execution
- host an angr training for a ctf team
- prepare for binary exploitation ctf categories
- teach students automated binary analysis

## When to choose
- you want hands-on, level-based practice with angr
- you are organizing a CTF or training session and need deployable challenge binaries
- you are a beginner learning symbolic execution on x86 binaries

## When to avoid
- you need a production reverse-engineering tool rather than exercises
- you want challenges outside binary/symbolic-execution topics
- you need polished documentation - the README is still incomplete

## Facets
- artifact type: learning-resource
- maturity: active
- function: reverse-engineering, security, developer-tools
- domain: security, education, reverse-engineering, tutorials
- platform: python, cli
- tags: ctf, angr, symbolic-execution, binary-analysis, capture-the-flag, challenges, linux

## Member repositories
- jakespringer/angr_ctf (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:40.589289+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 2026-08-30T06:40:52.810988+00:00, confidence not recorded.
  - readme: https://github.com/jakespringer/angr_ctf (fetched 2026-08-28T04:03:40.589289+00:00, sha 531b553be876)
- Data as of 2026-08-30T08:39:29.467469+00:00.