arXiv:2605.22737v1 Announce Type: new Abstract: Distillation attacks create a deployment trade-off for model providers: the same outputs that make a model more useful can also make it easier to imitate. We study this trade-off through a minimax game between a utility-constrained teacher and an adaptive student. Our framework yields tractable one-sided response rules: an adaptive evaluation rule in which the student reweights high-value examples, and a teacher-side defense template that suppresses outputs most useful for distillation. From a cheap proxy for example value, we derive Product-of-E
This research emerges as AI models become increasingly powerful and widely deployed, making the security and proprietary value of their outputs critical for model providers.
A strategic reader should care because this research directly addresses the vulnerability of proprietary AI models to imitation through distillation attacks, impacting intellectual property and competitive advantage.
This research provides a concrete framework for model providers to develop adaptive defenses against distillation attacks, shifting the balance in the 'distillation game'.
- · AI model developers
- · Cybersecurity researchers
- · Proprietary AI platforms
- · Malicious actors
- · Organizations relying on model imitation
Increased difficulty for attackers to distill proprietary AI models.
Enhanced intellectual property protection for AI developers, fostering investment in more advanced models.
A potential arms race between AI defense and attack methodologies, requiring continuous innovation in security.
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Read at arXiv cs.LG