This research provides a systematic analysis of safety threats and defense mechanisms for large AI models across various applications.

• Identifies major safety risks including adversarial attacks, data poisoning, backdoor vulnerabilities, and jailbreak techniques
• Maps safety challenges to different stages of the model lifecycle: pre-training, fine-tuning, and deployment
• Establishes a unified framework for categorizing and addressing safety concerns
• Discusses implementation challenges and future research directions for secure AI development

For security professionals, this research offers critical insights into protecting large models that now power critical infrastructure, sensitive applications, and key business systems.

Safety at Scale: A Comprehensive Survey of Large Model Safety