ai--llm-security

# AI & LLM Security ## Purpose Enable Claude to assess the security of AI/LLM-powered applications — chatbots, RAG pipelines, autonomous agents, and tool-using systems. Claude maps findings to the **OWASP Top 10 for LLM Applications (2025)** and the **MITRE ATLAS** adversarial-ML knowledge base, builds reproducible attack cases, and recommends concrete mitigations (input/output guardrails, least-privilege tool scopes, content provenance). > **Authorization Required**: Only test AI systems you own or are explicitly authorized to assess. Prompt-injection and data-exfiltration testing against third-party AI services may violate their terms of service and local law. Confirm written scope before proceeding. --- ## Activation Triggers This skill activates when the user asks about: - Prompt injection (direct or indirect), jailbreaks, or system-prompt extraction - OWASP LLM Top 10, MITRE ATLAS, or AI/ML threat modeling - Securing a RAG pipeline, vector database, or retrieval layer - LLM agent / tool-use / function-calling security and confused-deputy risks - Guardrail, content-filter, or model output validation design - Sensitive-information disclosure or training-data leakage from a model - Model / ML supply chain security (model files, `pickle`, model registries) - AI red teaming, jailbreak corpora, or automated adversarial prompt generation - Securing MCP (Model Context Protocol) servers and tool integrations --- ## Prerequisites ```bash pip install requests pyyaml rich `...