# jbangert/trapcc

Computing with traps

Repository: https://github.com/jbangert/trapcc
Canonical: https://ross.abutalabs.com/products/trapcc
Language: C
License: BSD-3-Clause
License Family: permissive
Last push: 2013-04-16T18:57:05+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": 4994, "days_push": 4887, "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 1324, forks 49 (observed 2026-08-28T04:04:22.327296+00:00)

## What it is
trapcc is a proof-by-construction that Intel MMU fault handling is Turing complete: an assembler translates 'Move, Branch if Zero, Decrement' programs into C that sets up processor control tables so the CPU computes purely via traps without executing instructions. It targets x86 bare metal or Bochs and can interleave trap-based computation with native display code.

## Use cases
- demonstrate turing completeness of mmu fault handling
- obfuscate code so debuggers break
- research weird machines and hidden state computation
- distinguish bochs emulator from real hardware
- run programs with zero executed instructions on x86

## When to choose
- studying weird machines or trap-based computation
- building anti-debugging or obfuscation research
- exploring x86 mmu and page fault behavior

## When to avoid
- building production software
- needing portable or maintained tooling
- running on non-x86 hardware or modern vms

## Facets
- artifact type: library
- maturity: maintenance
- function: compiler, interpreter, programming-language, reverse-engineering, security
- domain: security, compilers, operating-systems
- platform: cpp, embedded
- tags: weird-machine, mmu-traps, turing-completeness, code-obfuscation, x86, proof-of-concept, bochs, research, algorithms, linux

## Member repositories
- jbangert/trapcc (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:22.327296+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 2026-08-30T04:47:00.751844+00:00, confidence not recorded.
  - readme: https://github.com/jbangert/trapcc (fetched 2026-08-28T04:04:22.327296+00:00, sha 4f0224201a91)
- Data as of 2026-08-30T08:39:29.467469+00:00.