Impact: MITRE ATLAS

Adversaries can Craft Adversarial Data that prevents an AI model from correctly identifying the contents of the data or Generate Deepfakes that fools an AI model expecting authentic data.

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 significant amounts of specialized compute, they are often expensive bottlenecks that can become overloaded. Adversaries can intentionally craft inputs that require heavy amounts of useless compute from the AI system.

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 organization wasting time and money both attempting to fix the system and performing the tasks it was meant to automate by hand.

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 achieved via high-volume, low-complexity queries (Excessive Queries) or low-volume, high-complexity queries (Resource-Intensive Queries). In Generative AI or Agentic AI systems, adversarial prompts may be introduced into the model's context to cause (Agentic Resource Consumption).

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 waste time reviewing and correcting incorrect inferences.

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. These harms could affect the organization (e.g. Financial Harm, Reputational Harm), its users (e.g. User Harm), or the general public (e.g. Societal Harm).

Adversaries may poison or manipulate portions of a dataset to reduce its usefulness, reduce trust, and cause users to waste resources correcting errors.

Adversaries may invoke an AI agent's tool capable of performing mutative operations to perform Data Destruction. Adversaries may destroy data and files on specific systems or in large numbers on a network to interrupt availability to systems, services, and network resources.

Adversaries may compromise a machine by exploiting or manipulating AI-enabled components on the system. Compromising a victim system allows the adversary to execute arbitrary code, steal credentials, exfiltrate data, and continue to persist on the system.