Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety

This project is an academic survey paper (ArXiv: 2502.05206) rather than a software tool. While it has a high fork count (48) relative to its age (3 days), which suggests significant interest from research communities or automated archival bots, it lacks a technical moat. In the rapidly evolving field of AI safety, survey papers have a very short half-life (displacement horizon < 6 months) as new jailbreaking techniques and agentic vulnerabilities are discovered weekly. The project's value lies in its synthesis of existing literature, specifically bridging the gap between static LLM safety and dynamic agentic safety. Frontier labs like Anthropic and OpenAI are the primary 'competitors' in this space, as their technical reports and safety evaluations define the state-of-the-art that researchers are trying to survey. There is no software to 'defend'; the defensibility score is low because the information is public and easily superseded by subsequent literature reviews or official lab documentation.