Back to browse
GitHub Repository

Spec + offline verifier for Manifest-InX Evidence Bundles (EBS) v0.1: capture/pin artifacts, replay deterministically offline, reject drift/tamper.

2 starsPython

Manifestinx-verify – offline verifier for evidence bundles (drift)

by oneinx·Feb 20, 2026·2 points·0 comments

AI Analysis

●●SolidBig BrainSolve My Problem

Deterministic offline tamper detection—pinned at capture, replayed without side effects.

Strengths
  • Solves a real audit/compliance pain: nondeterministic captures now rejected deterministically.
  • Thoughtful boundaries: verifier/spec public, gateway private—lets skeptics validate independently.
  • 10-minute proof kit removes friction: skeptic check is immediately runnable, not hand-waved.
Weaknesses
  • Narrow audience: forensics/compliance teams only; won't appeal to general developers.
  • Ecosystem incomplete: spec-first approach is clever, but real value locked in closed Gateway runtime.
Category
Target Audience

DevSecOps engineers, compliance teams, forensics specialists

Similar To

Sigstore (code artifact transparency) · The Update Framework (TUF, pinned artifacts)

Post Description

Manifest-InX EBS is a spec + offline verifier + proof kit for tamper-evident evidence bundles.

Non-negotiable alignment: - Live provider calls are nondeterministic. - Determinism begins at CAPTURE (pinned artifacts). - Replay is deterministic offline. - Drift/tamper is deterministically rejected.

Try it in typically ~10 minutes (no signup): 1) Run the verifier against the included golden bundle → PASS 2) Tamper an artifact without updating hashes → deterministic drift/tamper rejection

Repo: https://github.com/OneInX/Manifest-InX-EBS Skeptic check: docs/ebs/PROOF_KIT/10_MINUTE_SKEPTIC_CHECK.md

Exit codes: 0=OK, 2=DRIFT/TAMPER, 1=INVALID/ERROR

Boundaries: - This repo ships verifier/spec/proof kit only. The Evidence Gateway (capture/emission runtime) is intentionally not included. - This is not a “model correctness / no hallucinations” claim—this is evidence integrity + deterministic replay/verification from pinned artifacts.

Looking for feedback: - Does the exit-code model map cleanly to CI gate usage? - Any spec/report format rough edges that block adoption?

Similar Projects

Infrastructure●●Solid

Make AI and automation pipelines fail-closed

Deterministic offline verification of AI pipeline outputs with Merkle hashing—novel framing, early stage.

Big BrainZero to One
oneinx
112mo ago