MITRE ATLAS 2026.09 / technique reference
AML.T0043
Craft Adversarial Data
MITRE source definition
Adversarial data are inputs to an AI model that have been modified such that they cause the adversary's desired effect in the target model. Effects can range from misclassification, to missed detections, to maximizing energy consumption. Typically, the modification is constrained in magnitude or location so that a human still perceives the data as if it were unmodified, but human perceptibility may not always be a concern depending on the adversary's intended effect. For example, an adversarial input for an image classification task is an image the AI model would misclassify, but a human would still recognize as containing the correct class.
Depending on the adversary's knowledge of and access to the target model, the adversary may use different classes of algorithms to develop the adversarial example such as [White-Box Optimization](/techniques/AML.T0043.000), [Black-Box Optimization](/techniques/AML.T0043.001), [Black-Box Transfer](/techniques/AML.T0043.002), or [Manual Modification](/techniques/AML.T0043.003).
The adversary may perform [Verify Attack](/techniques/AML.T0042) to confirm that their approach works if they have white-box or inference API access to the model. This allows the adversary to gain confidence their attack is effective in a live environment where their attack may be noticed. They can then use the attack at a later time to accomplish their goals. An adversary may optimize adversarial examples for [Evade AI Model](/techniques/AML.T0015), or to [Erode AI Model Integrity](/techniques/AML.T0031).
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
Source-backed defensive context
MITRE mitigations
- AML.M0002 Predictive AI Output Obfuscation
- AML.M0003 Predictive AI Model Hardening
- AML.M0004 Limit AI Service Query Volume and Rate
- AML.M0006 Predictive AI Ensembles
- AML.M0008 Validate AI Model
- AML.M0010 Predictive AI Input Restoration
- AML.M0015 Predictive AI Adversarial Input Detection
- AML.M0019 Control Access to AI Models and Data in Production
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
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No explicit relationship in this pinned source.