MITRE ATLAS 2026.09 / technique reference
AML.T0111
AI Supply Chain Reputation Inflation
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MITRE source definition
AI Supply Chain Reputation Inflation is the process of building or leveraging genuinely credible-looking trust signals to increase the perceived legitimacy of AI supply chain components, with the goal of driving adoption of malicious or compromised assets.
Adversaries use established developer accounts with a history of legitimate projects and contributions to publish AI models, datasets, packages, and MCP servers that appear trustworthy. They build reputation through real adoption signals such as downloads, GitHub stars, forks, and inclusion in dependency chains, often releasing benign versions before introducing malicious updates via AI Supply Chain Rug Pull.
By relying on authentic history and usage patterns, these components pass both human and automated trust checks, increasing the likelihood they are adopted without scrutiny.
Source modified 2026-05-27. Reproduced from the pinned ATLAS release; inline technique links resolve to local reference pages.
Parent, sub-techniques and ATT&CK references
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.
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.T0111 AI Supply Chain Reputation Inflation. 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.T0111",
"simulation": false,
"target": "REQUIRES_OWNED_LAB",
"source_scenario": [
"AML.CS0049"
],
"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.