Detect when an LLM silently changes behavior for the same prompt
Cryptographic proof of AI outputs when compliance teams ask what the model actually said.
Spec + offline verifier for Manifest-InX Evidence Bundles (EBS) v0.1: capture/pin artifacts, replay deterministically offline, reject drift/tamper.
Deterministic offline tamper detection—pinned at capture, replayed without side effects.
DevSecOps engineers, compliance teams, forensics specialists
Sigstore (code artifact transparency) · The Update Framework (TUF, pinned artifacts)
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?
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