# model-weights
標記為「model-weights」的 6 篇文章
Model State Snapshots
Techniques for capturing and preserving AI model state during incident response: weight snapshots, configuration capture, behavioral fingerprinting, and model artifact integrity verification.
Open vs Closed Models: Security Tradeoffs
Security implications of open-weight vs closed-source AI models — weight access, responsible deployment, fine-tuning risks, and the impact on red teaming strategy.
Model Checkpoint & Recovery Attacks
Checkpoint file format vulnerabilities, modification attacks on safetensors and PyTorch formats, checkpoint poisoning, storage security, and supply chain implications.
模型 State Snapshots
Techniques for capturing and preserving AI model state during incident response: weight snapshots, configuration capture, behavioral fingerprinting, and model artifact integrity verification.
開放 vs 封閉模型：安全權衡
開放權重 vs 封閉原始碼 AI 模型之安全意涵——權重存取、負責任部署、微調風險，與對紅隊策略之影響。
模型 Checkpoint 與復原攻擊
Checkpoint 檔案格式漏洞、對 safetensors 與 PyTorch 格式之修改攻擊、checkpoint 投毒、儲存安全，以及供應鏈意涵。