A curated collection of safety-related papers, articles, and resources focused on Large Language Models — comprehensive reference for researchers and practitioners exploring LLM safety implications and advancements.
Meta's set of tools to assess and improve LLM security, including safety benchmarks, prompt injection detection, and output auditing to help evaluate and enhance the safety of large language models.
The Python Risk Identification Tool for generative AI — an open-source framework by Microsoft for proactively identifying risks in generative AI systems through red teaming and automated probing.
AI agent security scanner that detects vulnerabilities in agent configurations, MCP servers, and tool permissions. Available as CLI, GitHub Action, and GitHub App integration.
An automated penetration testing agentic framework powered by large language models for security testing and vulnerability discovery.