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 achieved via high volume, low complexity queries (Excessive Queries) or low volume, high complexity queries (Resource Intensive Queries). In Generative AI or Agentic
- Framework
- MITRE ATLAS
- Maturity
- Feasible
- Platforms
- Predictive AI, Generative AI, Agentic AI
- Release
- 2026.05
Overview
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).
Unlike resource hijacking, where adversaries may leverage AI resources such as computational, memory, or storage for their own purposes, cost harvesting focuses on resource-centric pressure to a service to ultimately cause financial harm to the victim.
Cost Harvesting is especially relevant for cloud-hosted, pay-per-use AI/ML platforms (e.g., LLM APIs, generative image services, vision-language pipelines). By manipulating request volume or request complexity, an attacker can:
- Inflate the victim's compute or storage consumption, leading to higher operational costs.
- Trigger autoscaling mechanisms that provision additional resources, further amplifying cost and exposure.
- Saturate internal queues or GPU/TPU pipelines, causing latency spikes, request throttling, or outright service unavailability for legitimate users.