MITRE ATLAS 2026.09 / technique reference

AML.T0034
Cost Harvesting

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MITRE source definition

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](/techniques/AML.T0034.000)) or low-volume, high-complexity queries ([Resource-Intensive Queries](/techniques/AML.T0034.001)). In Generative AI or Agentic AI systems, adversarial prompts may be introduced into the model's context to cause ([Agentic Resource Consumption](/techniques/AML.T0034.002)).

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.

Source modified 2026-05-27. Reproduced from the pinned ATLAS release; inline technique links resolve to local reference pages.

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