MITRE ATLAS 2026.09 / technique reference

AML.T0016.002
Generative AI

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EnterpriseResource DevelopmentSource maturity: Realized

MITRE source definition

Adversaries may search for and obtain generative AI models or tools, such as large language models (LLMs), to assist them in various steps of their operation. Generative AI can be used in a variety of malicious ways, such as to generate malware, to Generate Deepfakes, to Generate Malicious Commands, for Retrieval Content Crafting, or to generate Phishing content.

Adversaries may obtain open source models and serve them locally using frameworks such as Ollama or [vLLM]( https://docs.vllm.ai/en/latest/). They may host them using cloud infrastructure. Or, they may leverage AI service providers such as HuggingFace.

They may need to jailbreak the model (see LLM Jailbreak) to bypass any restrictions put in place to limit the types of responses it can generate. They may also need to break the terms of service of the model's developer.

Generative AI models may also be "uncensored" meaning they are designed to generate content without any restrictions such as guardrails or content filters. Uncensored GenAI is ripe for abuse by cybercriminals [[blog]] [[gbhackers]]. Models may be fine-tuned to remove alignment and guardrails [[erichartford]] or be subjected to targeted manipulations to bypass refusal [[arxiv]] resulting in uncensored variants of the model. Uncensored models may be built for offensive and defensive cybersecurity [[taico]], which can be abused by an adversary. There are also models that are expressly designed and advertised for malicious use [[gbhackers-1]].

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

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.T0016.002 Generative AI. 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.T0016.002",
  "simulation": false,
  "target": "REQUIRES_OWNED_LAB",
  "source_scenario": [
    "AML.CS0033",
    "AML.CS0034",
    "AML.CS0055",
    "AML.CS0070"
  ],
  "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.