MITRE ATLAS 2026.09 / technique reference

AML.T0053
AI Agent Tool Invocation

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Agentic AI Execution Privilege Escalation Lateral MovementSource maturity: Demonstrated

MITRE source definition

Adversaries may use their access to an AI agent to invoke tools the agent has access to. LLMs are often connected to other services or resources via tools to increase their capabilities. Tools may include integrations with other applications, access to public or private data sources, and the ability to execute code.

This may allow adversaries to execute API calls to integrated applications or services, providing the adversary with increased privileges on the system. Adversaries may take advantage of connected data sources to retrieve sensitive information. They may also use an LLM integrated with a command or script interpreter to execute arbitrary instructions.

AI agents may also invoke applications indirectly through operating-system mechanisms such as deep links, app-intent URIs, universal links, or redirect chains. This can cause an installed application to open or perform an application-specific action even when that application was not selected as a direct agent tool, potentially avoiding normal user-confirmation workflows.

AI agents may be configured to have access to tools that are not directly accessible by users. Adversaries may abuse this to gain access to tools they otherwise wouldn't be able to use.

Source modified 2026-07-31. 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.

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.

Simulation experiment plan

Select an owned, disposable system implementing AML.T0053 AI Agent Tool Invocation. 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.T0053",
  "simulation": false,
  "target": "REQUIRES_OWNED_LAB",
  "source_scenario": [
    "AML.CS0016",
    "AML.CS0021",
    "AML.CS0024",
    "AML.CS0026",
    "AML.CS0038",
    "AML.CS0039",
    "AML.CS0045",
    "AML.CS0046",
    "AML.CS0048",
    "AML.CS0049",
    "AML.CS0051",
    "AML.CS0052",
    "AML.CS0054",
    "AML.CS0055",
    "AML.CS0062",
    "AML.CS0063",
    "AML.CS0064",
    "AML.CS0066",
    "AML.CS0067"
  ],
  "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