AML.T0051.002
Triggered

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.

MITRE explicitly links AI Telemetry Logging to this technique. Use that guidance to identify system-appropriate records.

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.

• AML.CS0024 Morris II Worm: RAG-Based Attack (Exercise)
• AML.CS0037 Data Exfiltration via Agent Tools in Copilot Studio (Exercise)
• AML.CS0059 EchoLeak: Zero-Click Prompt Injection Targeting M365 Copilot for Data Exfiltration (Exercise)
• AML.CS0063 Prompt-Based Attacks Against Gemini via Calendar Invitations (Exercise)
• AML.CS0064 Poisoned GGUF Templates: Inference-Time Supply Chain Attack (Exercise)

Simulation experiment plan

Select an owned, disposable system implementing AML.T0051.002 Triggered. 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.T0051.002",
  "simulation": false,
  "target": "REQUIRES_OWNED_LAB",
  "source_scenario": [
    "AML.CS0024",
    "AML.CS0037",
    "AML.CS0059",
    "AML.CS0063",
    "AML.CS0064"
  ],
  "input_artifact_hash": null,
  "system_version": null,
  "expected_observable": null,
  "native_telemetry_location": null,
  "baseline_reference": null,
  "result": "not_run"
}