I built a cryptographic audit receipt for Claude Mythos (and any AI model) — here's how it works

Anthropic's Mythos model can autonomously find zero-day

vulnerabilities. Their CVD disclosure process uses manual

SHA-3-512 hash commitments to prove findings existed.

I built something that automates that in one line of Python.

One function call generates a 128-byte Ed25519-signed receipt

that proves:

python
import aetherproof

receipt = aetherproof.for_anthropic(
    "Find vulnerabilities in this binary.",
    finding_text,
    model="claude-mythos-preview"
)

receipt.save("CVE-2026-001.receipt")
print(receipt.verify())  # True
Try it in 30 seconds

pip install aetherproof
python -c "
import aetherproof
r = aetherproof.for_anthropic('question', 'answer')
print(r.verify())   # True
print(r.pretty())
"
The unusual part — invisible Unicode watermarking
Receipts embed invisibly into any text using Unicode
Private Use Area codepoints (U+E000–U+E0FF).

AI output carries its own audit trail. Works in any language —
Arabic, Chinese, Devanagari, Hebrew, Thai, Japanese all tested.

signed_output = aetherproof.embed(ai_response, receipt.to_bytes())

aetherproof.verify_embedded(signed_output)  # True
Numbers
187 tests, 0 failures
128/128 byte flips all detected
1000/1000 tamper probes pass
Cross-language: Python generates, Rust CLI verifies
15,446 receipts/sec (Python) · 5,472/sec (Rust)
Why AGPL-3.0
Free for open source. Commercial use needs a license.
This is the compliance layer under your AI stack —
it should be open, auditable, and not vendor-locked.

GitHub
https://github.com/pulkit6732/aetherproof

Built by Pulkit. Feedback welcome.