- Maturity
- feasible
- Reference
- atlas.mitre.org/techniques/AML.T0034.001
Description
Adversaries may craft inputs specifically designed to increase the compute resources required for processing.
For generative AI models, adversaries may use long input sequences, requests for extremely long outputs, or prompts that require complex reasoning as strategies for increasing compute costs [1]. For vision and language models, “sponge examples” [2] can be used to maximize energy consumption and decision latency. Utilizing fewer resource-intensive queries instead of simply flooding the model with excessive queries may be more difficult to detect and block or limit.
How GTK Cyber trains on this
GTK Cyber's hands-on AI security courses cover adversarial-AI techniques across the MITRE ATLAS framework, including the relevant tactic this technique falls under. Our practitioner-led training is taught by Charles Givre and other field-tested SMEs and focuses on real adversarial scenarios, not slide decks.