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.

Current LLM-based multi-agent systems remain fragile under scaling, even on algorithmically trivial tasks. We introduce MAS-BENCH, a distributed-sorting benchmark that isolates coordination under explicit communication constraints: each agent observes only a local segment and must collectively produce a globally consistent order via broadcasting, peer-to-peer messaging, or a shared key-value store. Across LLM-based agents, success drops sharply as the number of agents grows, exposing persistent failures in shared state, convention alignment, and consistent termination. To mitigate these breakdowns, we propose CAMOC, a lightweight, drop-in proof-of-concept built on collaboration-aware information sharing, early global metadata exchange, and single-commit verification. CAMOC substantially improves coordination success and efficiency across backends, with the largest gains under shared-state interaction. Overall, MAS-BENCH provides a diagnostic benchmark and CAMOC offers a practical step toward more reliable large-scale LLM collaboration, highlighting a gap between individual reasoning and collective correctness.