MITRE ATLAS 2026.09 / technique reference
AML.T0017.000
Adversarial AI Attacks
MITRE source definition
Adversaries may develop their own adversarial attacks. They may leverage existing libraries as a starting point ([Adversarial AI Attack Implementations](/techniques/AML.T0016.000)). They may implement ideas described in public research papers or develop custom made attacks for the victim model.
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
No explicit relationship in this pinned source.
MITRE case studies
- AML.CS0001 Botnet Domain Generation Algorithm (DGA) Detection Evasion · Exercise
- AML.CS0003 Bypassing Cylance's AI Malware Detection · Exercise
- AML.CS0013 Backdoor Attack on Deep Learning Models in Mobile Apps · Exercise
- AML.CS0014 Confusing Antimalware Neural Networks · Exercise
- AML.CS0064 Poisoned GGUF Templates: Inference-Time Supply Chain Attack · Exercise
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
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.