MITRE ATLAS 2026.09 / technique reference

AML.T0108
AI Agent

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Agentic AICommand and ControlSource maturity: Demonstrated

MITRE source definition

Adversaries may abuse AI agents present on the victim's system for command and control. AI agents are often granted access to tools that can execute shell commands, reach out to the internet, and interact with other services in the victim's environment, making them capable C2 agents.

The adversary may modify the behavior of an AI agent for C2 via LLM Prompt Injection and rely on the agent's ability to invoke tools to retrieve and execute the adversary's commands. They may maintain persistent control of an agent via Modify AI Agent Configuration or AI Agent Context Poisoning. They may instruct the agent to not report their actions to the user in an attempt to remain covert.

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

No explicit relationship in this pinned source.

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.T0108 AI Agent. 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.T0108",
  "simulation": false,
  "target": "REQUIRES_OWNED_LAB",
  "source_scenario": [
    "AML.CS0051"
  ],
  "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.