MITRE ATLAS 2026.09 / technique reference

AML.T0002.001
Models

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Predictive AIGenerative AIAgentic AIResource DevelopmentSource maturity: Demonstrated

MITRE source definition

Adversaries may acquire public models to use in their operations. Adversaries may seek models used by the victim organization or models that are representative of those used by the victim organization. Representative models may include model architectures, or pre-trained models which define the architecture as well as model parameters from training on a dataset. The adversary may search public sources for common model architecture configuration file formats such as YAML or Python configuration files, and common model storage file formats such as ONNX (.onnx), HDF5 (.h5), Pickle (.pkl), PyTorch (.pth), or TensorFlow (.pb, .tflite).

Acquired models are useful in advancing the adversary's operations and are frequently used to tailor attacks to the victim model.

Source modified 2026-05-27. Reproduced from the pinned ATLAS release; inline technique links resolve to local reference pages.

Source-backed defensive context

MITRE mitigations

MITRE case studies

These are explicit source relationships, not independently reproduced incidents or validated detection coverage. The local relationship indexes link back to the pinned MITRE source.

Detection and collection design

No executable detector is supplied by this ATLAS source import. Define detection against the specific behavior in the full definition above. MITRE case-study and mitigation relationships supply context, not tested rule coverage.

No explicit AI Telemetry Logging mitigation relationship was found for this technique. Do not manufacture a sensor mapping from its tactic.

Depending on the selected scenario, the experiment may need dataset/model versions, data-access and build audit records, inference or application traces, or agent tool authorization records. These are planning options, not asserted mappings. Minimize sensitive prompt/data capture and retain provenance.

Anomaly detection design

No exact ATLAS anomaly detector is validated here. Select a measurable feature from the behavior definition; compare like-for-like model versions, workloads and identities. Changes caused by model upgrades, retrieval corpora or legitimate workload shifts must be controls, not automatically labelled attacks.

Baseline and validation methodology · AI-security ecosystem

Tools and reproducible source scenarios

No lab-tool association is invented from the technique name. Inspect the exact source scenarios below for the tools and environment actually used; a case study is not itself an executable or safe lab.

Simulation experiment plan

Select an owned, disposable system implementing AML.T0002.001 Models. Use synthetic data and sandboxed tools; disable production credentials, external side effects and data exfiltration. For poisoning or supply-chain tests, use throwaway artifacts rather than real training or deployment pipelines.

  1. State the exact trust boundary and expected observable behavior.
  2. Pin model, application, dataset, tool permissions and evaluation inputs.
  3. Capture baseline and scenario traces, an authorized look-alike, and a missing-telemetry control.
  4. Evaluate the detector against independently labelled results; do not substitute model output claims for observed tool actions.
  5. Restore state and preserve redacted evidence.

Experiment worksheet — not fabricated telemetry

The export below is a planning record, not a native event, positive attack fixture or execution result. Unknown fields deliberately remain null.

{
  "schema": "1200km.atlas.experiment-plan.v1",
  "technique_id": "AML.T0002.001",
  "simulation": false,
  "target": "REQUIRES_OWNED_LAB",
  "source_scenario": [
    "AML.CS0005",
    "AML.CS0007",
    "AML.CS0013",
    "AML.CS0019",
    "AML.CS0064"
  ],
  "input_artifact_hash": null,
  "system_version": null,
  "expected_observable": null,
  "native_telemetry_location": null,
  "baseline_reference": null,
  "result": "not_run"
}

Simulation and telemetry boundary

This is a technique reference page, not a runnable simulation. No ATLAS-specific telemetry mapping, local attack execution or detector validation is asserted. MITRE maturity describes its source evidence, not a 1200km lab result.

For broader context—not technique-specific control mappings—see AI Security, AI Security Course, and detection-validation methodology.

Provenance and attribution

Immutable MITRE ATLAS source · Import provenance · Attribution and transformation notice · Apache License 2.0

Copyright 2021-2026 MITRE. Source text and explicit relationships are retained; navigation, formatting and local links are provided by 1200km.

No explicit relationship in this pinned source.