MITRE ATLAS 2026.09 / technique reference

AML.T0020
Training Data Poisoning

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Predictive AI Generative AI Agentic AI PersistenceSource maturity: Realized

MITRE source definition

Adversaries may manipulate data used for training or fine-tuning an AI model to influence the resulting model's behavior. Adversaries may add, remove, or modify data samples; alter labels or annotations; or manipulate feedback and data-collection processes.

Training data poisoning may cause targeted errors, introduce biased or unsafe behavior, degrade model performance, or embed backdoors activated by specific inputs. The resulting behavior may persist in the trained model after the poisoned data is no longer accessible.

Adversaries may poison data directly after gaining access to a training pipeline, or poisoned datasets may be introduced through [AI Supply Chain Compromise](/techniques/AML.T0010).

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.

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