Open-weight large language models create demand for weight protection schemes that let users run models locally while preserving developer control over valuable model weights. Existing schemes, however, are often justified by heuristic arguments and empirical checks rather than explicit adversarial goals, security definitions, or proofs. As a case study, we analyze a recent scheme previously published in *EMNLP 2024 Main Conference* and show that it is vulnerable to our efficient weight-extraction attack, recovering more than 99.95% of protected parameters and producing reconstructed models with near-identical performance. Motivated by this failure, we formulate LLM weight protection schemes through game-based definitions, and we discuss how these notions relate. We encourage that future weight protection schemes should be designed with formal security goals, proof-oriented analysis, and offensive evaluation rather than relying on informal intuition alone.