Making Models Unmergeable via Scaling-Sensitive Loss Landscape
Researchers have developed Trap$^2$, a new framework designed to prevent unauthorized model merging in AI. This architecture-agnostic system encodes protection directly into fine-tuned weights, degrading them when they are recomposed into unauthorized mixtures. Trap$^2$ aims to address a governance gap created by model hubs, ensuring that released weights remain effective for standalone use while undermining attempts to bypass safety alignments or licensing terms through merging. AI
IMPACT Provides a technical solution to prevent misuse of released AI models through unauthorized merging.