DARKGPT.AI

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THREAT MODEL

Assume everyone is watching. Design so it doesn't matter.

DarkGPT assumes the provider is curious, the network is observed, and the chain is public. Here is exactly what each of them can see.

Who sees what

              OBSERVERWHAT THEY GETWHAT THEY NEVER GET
            

The providerCiphertext, a model name, a paid jobYour prompt, your identity

The networkEncrypted packets between rotating peer IDsContent, stable identifiers

The blockchainAn escrow opened and settledWho asked what, and of whom

UsNothing. There is no us in the request pathEverything

TRANSPORT

Noise Protocol, ChaCha20-Poly1305

Every peer connection is authenticated and encrypted end to end with ephemeral session keys, so a captured session cannot be replayed or decrypted later.

KEYS

Apple Secure Enclave

Wallet and decryption keys are generated inside the enclave with hardware attestation. They never touch the host OS, and high value transfers require biometric approval.

EXECUTION

Sealed VM, verified binaries

Inference runs in an isolated VM against a hash verified Ollama build. Plaintext exists only inside that boundary, and only for the life of the request.

SETTLEMENT

Escrow with nonces and proof registry

Each escrow carries a unique nonce and each proof can be spent once, which closes double payment on both sides. Unproven work refunds automatically at ten minutes.

What we do not claim

Honest limits, because a security page that only lists strengths is marketing, not a threat model.

- A malicious provider can refuse work, not read it
- Traffic timing is randomised, not perfectly hidden
- Model output quality is not guaranteed by any proof
- Your own machine is still your own responsibility

Read the code, then decide

The client, the provider node, the circuits and the contracts are all MIT licensed and public. Disclosure reports go to the security address and are triaged within 48 hours.

GITHUB DISCLOSURE

          DARKGPT.AI / MIT LICENSED / OPEN SOURCE
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