This research establishes a systematic framework for understanding and addressing safety risks in large AI models across diverse applications.

• Threat Landscape: Identifies key vulnerabilities including adversarial attacks, data poisoning, backdoors, and jailbreak attempts
• Defense Mechanisms: Evaluates countermeasures for securing large models throughout their lifecycle
• Risk Assessment: Provides methodologies for early identification and mitigation of safety concerns
• Practical Applications: Offers security insights for conversational AI, autonomous systems, and medical applications

For security professionals, this research delivers actionable strategies to protect large-scale AI deployments while ensuring their reliability and trustworthiness in critical domains.

Safety at Scale: A Comprehensive Survey of Large Model Safety