MITRE ATLAS 2026.09 / technique reference
AML.T0002.000
Datasets
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MITRE source definition
Adversaries may collect public datasets to use in their operations. Datasets used by the victim organization or datasets that are representative of the data used by the victim organization may be valuable to adversaries. Datasets can be stored in cloud storage, or on victim-owned websites. Some datasets require the adversary to Establish Accounts for access.
Acquired datasets help the adversary advance their operations, stage attacks, and tailor attacks to the victim organization.
Source modified 2026-05-27. Reproduced from the pinned ATLAS release; inline technique links resolve to local reference pages.
Parent, sub-techniques and ATT&CK references
Source-backed defensive context
MITRE mitigations
MITRE case studies
- AML.CS0000 Evasion of Deep Learning Detector for Malware C&C Traffic · Exercise
- AML.CS0005 Attack on Machine Translation Services · Exercise
- AML.CS0007 GPT-2 Model Replication · Exercise
- AML.CS0012 Face Identification System Evasion via Physical Countermeasures · Exercise
- AML.CS0014 Confusing Antimalware Neural Networks · Exercise
- AML.CS0025 Web-Scale Data Poisoning: Split-View Attack · Exercise
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.
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.
- AML.CS0000 Evasion of Deep Learning Detector for Malware C&C Traffic (Exercise)
- AML.CS0005 Attack on Machine Translation Services (Exercise)
- AML.CS0007 GPT-2 Model Replication (Exercise)
- AML.CS0012 Face Identification System Evasion via Physical Countermeasures (Exercise)
- AML.CS0014 Confusing Antimalware Neural Networks (Exercise)
- AML.CS0025 Web-Scale Data Poisoning: Split-View Attack (Exercise)
Simulation experiment plan
Select an owned, disposable system implementing AML.T0002.000 Datasets. 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.
- State the exact trust boundary and expected observable behavior.
- Pin model, application, dataset, tool permissions and evaluation inputs.
- Capture baseline and scenario traces, an authorized look-alike, and a missing-telemetry control.
- Evaluate the detector against independently labelled results; do not substitute model output claims for observed tool actions.
- 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.T0002.000",
"simulation": false,
"target": "REQUIRES_OWNED_LAB",
"source_scenario": [
"AML.CS0000",
"AML.CS0005",
"AML.CS0007",
"AML.CS0012",
"AML.CS0014",
"AML.CS0025"
],
"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.