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POST
Attest Model
Creates a cryptographically anchored attestation for an AI model. The attestation captures the model’s framework, training lineage, hyperparameters, and evaluation metrics. It is linked to the originating agent and anchored in the transparency log. Model attestations provide an auditable record of what was trained, how it was trained, and what data was used — enabling compliance teams and downstream consumers to verify model provenance before deployment.

Response

string
Unique model attestation identifier in MAIP format (maip-model:ULID).
string
The agent that attested this model.
string
Model name as provided in the request.
string
Resolved version of the model.
string
ML framework identifier.
string
SHA-256 hex digest stored for integrity verification.
string[]
Linked training dataset attestation identifiers.
string
Attestation status. Always attested on creation.
string
Transparency-log receipt anchoring this attestation.
string
ISO 8601 timestamp of creation.

Authorizations

X-API-Key
string
header
required

API key for machine-to-machine authentication

Body

application/json
agent_id
string<uuid>
required

Attesting agent

model_hash
string
required
model_name
string
required
architecture
string | null
framework
string

ML framework (e.g. pytorch, tensorflow, onnx)

hash_algorithm
string
hyperparameters
any
input_schema
any
metadata
any
metrics
any
model_version
string
output_schema
any
previous_version_id
string | null
tags
string[]
training_dataset_ids
string<uuid>[]

IDs of attested training datasets

Response

Model attested

model_id
string<uuid>
receipt_id
string<uuid>
name
string
hash
string
framework
string
version
string
agent_id
string<uuid>
training_dataset_ids
string<uuid>[]
created_at
string<date-time>