101 indexed
ATLASATLAS adversarial ML techniques
101 MITRE ATLAS top-level techniques covering the adversarial-ML attack surface, grouped by tactic. Authored by Adam Lundqvist.
8 in Impact · 101 total
| ID | Title | Summary |
|---|---|---|
| AML.T0029 | Denial of AI Service | Adversaries may target AI-enabled systems with a flood of requests for the purpose of degrading or shutting down the service. Since many AI systems require sig… |
| AML.T0031 | Erode AI Model Integrity | Adversaries may degrade the target model's performance with adversarial data inputs to erode confidence in the system over time. This can lead to the victim or… |
| AML.T0034 | Cost Harvesting | Adversaries may deliberately drive a victim's AI services beyond normal operating capacity with the intent of increasing the cost of services. This may be achi… |
| AML.T0046 | Spamming AI System with Chaff Data | Adversaries may spam the AI system with chaff data that causes increase in the number of detections. This can cause analysts at the victim organization to wast… |
| AML.T0048 | External Harms | Adversaries may abuse their access to a victim system and use its resources or capabilities to further their goals by causing harms external to that system. Th… |
| AML.T0059 | Erode Dataset Integrity | Adversaries may poison or manipulate portions of a dataset to reduce its usefulness, reduce trust, and cause users to waste resources correcting errors. |
| AML.T0101 | Data Destruction via AI Agent Tool Invocation | Adversaries may invoke an AI agent's tool capable of performing mutative operations to perform Data Destruction. Adversaries may destroy data and files on spec… |
| AML.T0112 | Machine Compromise | Adversaries may compromise a machine by exploiting or manipulating AI-enabled components on the system. Compromising a victim system allows the adversary to ex… |