The Problem: The AI Stack Is Incomplete

Today’s LLM workflows are built on opaque assumptions.

You send a prompt to an API. You get a response. But in between?

• You don’t know if your prompt was logged

• You can’t prove the model wasn’t tampered with

• You have no control over where your data travels

This blind trust model might have been acceptable when LLMs wrote poems. But now?

LLMs interpret transactions, recommend votes, process medical queries, and assist legal reasoning. “Hope” isn’t enough.

Traditional LLM Access Is Not Trust-Minimized

Mainstream APIs like OpenAI and Claude are powerful but fully centralized. They see every request, log metadata, and operate as black boxes.

You can’t verify:

• What model was actually used

• Whether your prompt was altered

• How long your data is stored

• If the model was running in its expected state

Even in crypto-native use cases—like an AI-powered wallet asking “Can I afford this transaction?”—you’re placing complete trust in a cloud provider.

That model doesn’t scale with the stakes.

TEE-Attested LLM Relays

A TEE (Trusted Execution Environment) is a secure area within a processor that guarantees code is run in isolation and can't be tampered with—even by the host system.

When applied to an LLM relay, this changes everything.

Key Properties of a TEE-Attested Relay

• Encrypted Prompts: Your prompt is encrypted and only decrypted inside the TEE.

• Private Inference: The relay cannot inspect, store, or leak your prompt.

• Attested Execution: Every response includes proof that the correct model, code, and endpoint were used.

This creates a verifiable chain of trust from prompt to response.

Why It Matters

In high-stakes applications, unverified inference is a major liability. Without attestation, AI infrastructure can silently expose users to:

• Data leakage: Centralized relays may log, store, or monetize your data.

• Spoofed models: Endpoints may be rerouted to fine-tuned or malicious clones.

• Undetectable tampering: Models can be manipulated to hallucinate, censor, or mislead.

A TEE-attested relay eliminates these risks by enforcing execution transparency and data confidentiality at the hardware level.

Use Cases That Require Verifiable AI Inference

If you're building any of the following, TEE-attested inference isn't optional—it's foundational:

• AI-powered crypto wallets

• Onchain agents and smart contract logic

• DAO governance assistants

• AI moderation for decentralized communities

• DePIN, DeFi, and cross-chain coordination layers

• Reputation or identity systems with sensitive prompts

Without verifiable execution, all of these rely on blind trust in the infrastructure—not ideal when you're managing value, decisions, or rights.

From Hope to Proof

TEE-attested LLM relays represent a shift in AI architecture:

• From centralization → to distributed trust

• From black boxes → to auditable systems

• From “trust us” → to here’s the proof

This is how AI infrastructure should work when it matters.