A recurring point of contention in our threat modeling for agentic AI systems is the integrity of the model weights themselves. We can architect a pipeline where the inference occurs within a TEE, but the initial loading of the proprietary model presents a critical attack vector. If the weights are transmitted from an untrusted storage service to the TEE in plaintext, the entire enclave's value is arguably negated.

My current assessment focuses on the three platforms mentioned in the subforum title. The verification must cover the complete data lifecycle:
1. **At-rest encryption:** The weights must be encrypted with a key that is *only* accessible from within the TEE.
2. **Secure provisioning:** The encrypted model must be delivered to the enclave via a channel that guarantees the payload's origin and confidentiality.
3. **In-enclave decryption:** The decryption operation must be provably confined to the TEE's secure memory, with no key material or plaintext weights leaking via debug interfaces, memory snapshots, or side channels.

For Intel TDX and AMD SEV-SNP, this typically involves leveraging platform-specific remote attestation and sealing. The attestation report, signed by the processor's root of trust, provides a measurement of the initial enclave code. This measurement becomes the basis for deriving a unique, restricted key. Here is a conceptual flow using TDX:

```python
# Pseudocode for TDX model provisioning
import tdx_attestation

# 1. Generate attestation report from within the TD
attestation_report = tdx_attestation.get_report()
# Report includes TD measurement (MRENCLAVE) and runtime data.

# 2. Remote verifier checks report, derives a unique key for this specific TD
if verifier.validate(attestation_report):
# 3. Model provider encrypts weights with a key sealed to this TD's measurement
sealed_key = tdx_attestation.get_key(attestation_report.mrenclave)
encrypted_weights = aes_gcm_encrypt(plaintext_weights, sealed_key)

# 4. Encrypted weights are sent to the host application, which loads them into the TD.
# 5. Inside the TD, using the same sealed_key (derived internally from measurement):
decrypted_weights = aes_gcm_decrypt(encrypted_weights, sealed_key)
# The 'sealed_key' never exists in host memory.
```

AWS Nitro Enclaves uses a similar attestation pattern but rooted in the Nitro Hypervisor's certificate chain. The critical operational difference is the reliance on KMS for key management, where the `kms:Recipient` attribute in a decrypt call must be the enclave's attestation document.

The open questions I'm grappling with are practical verification steps:
* What are the definitive, platform-specific indicators that a memory page containing plaintext weights cannot be accessed by the host? For SEV-SNP, is the Reverse Map (RMP) table validation sufficient proof?
* How do we instrument or log this guarantee? Is a continuous attestation mechanism required, or is a one-time setup with measured boot sufficient?
* In a regulated deployment (e.g., handling PII), would a third-party auditor accept the attestation report from a proprietary cloud provider's TEE as evidence of non-exposure, or would they require additional hardware-based mechanisms?

I am particularly interested in experiences where this verification has failed or been circumvented, as that informs the real-world security property.