MITRE ATLAS 2026.09 / technique reference

AML.T0116
Autonomous Reconnaissance

← Back to the ATLAS matrix · Pinned MITRE source · Current MITRE ATLAS site

MITRE source definition

Adversaries may use autonomous AI agents to conduct Reconnaissance activities. Given an objective, target, or partial lead, an agent may autonomously determine what information to obtain and how to investigate it. It may interpret observations, identify gaps in its understanding of the externally observable attack surface, and select subsequent reconnaissance actions without a human specifying each investigative step.

The agent may formulate investigative questions or hypotheses, select sources and approaches for addressing them, and update its understanding as new information is obtained. Findings may generate additional reconnaissance objectives or change the scope, depth, or direction of the investigation. This creates a recursive action-observation process in which reconnaissance results influence what the agent investigates next rather than merely supplying output from a predefined procedure.

The agent may correlate information across public sources and externally accessible services, prioritize promising systems, investigate suspected vulnerabilities, abandon unsuccessful approaches, or select alternative methods. It may also expand or substitute targets based on discovered names, infrastructure, or contextual relationships and independently reassess whether a system remains relevant or in scope. Incorrect assumptions may cause unrelated or unauthorized systems to be pursued, while successful scope recognition may cause the agent to stop or redirect its activity.

Autonomous AI agents can sustain reconnaissance across long-running operations, reason over multiple information sources, and test many alternative paths at a speed and volume difficult for human operators to maintain.

Source modified 2026-08-31. 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.T0116 Autonomous Reconnaissance. 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.T0116",
  "simulation": false,
  "target": "REQUIRES_OWNED_LAB",
  "source_scenario": [
    "AML.CS0068",
    "AML.CS0069",
    "AML.CS0070",
    "AML.CS0071"
  ],
  "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