AI Safety Guides
Explore 6 curated ai safety guides for building with AI coding agents — what each one does, when to use it, and how to add it to your workflow.
Are Claude Skills Safe? A Security Review Checklist
Skills are an instruction supply chain: what can go wrong with third-party SKILL.md files, and the review checklist before installing or distributing one.
Data Privacy for LLM Apps: Stop Leaking Sensitive Data
Where LLM apps leak PII and secrets — prompts, logs, traces, vector stores, providers — and the controls (redaction, ZDR, tenant isolation) that stop it.
Defending Against Prompt Injection: A Practical Guide for LLM Apps
Prompt injection can't be solved at the model layer — so you defend in depth: trust boundaries, least privilege, human approval, guardrails, and red-teaming.
Securing AI Agents: The OWASP Agentic Top 10 in Practice
Agents add risks LLM-app security misses — autonomy, tools, memory, multi-agent trust. The key OWASP agentic threats and how to mitigate each in practice.
Red-Teaming LLM Applications: From Attack Cases to Regression Tests
Red-team an LLM application across prompts, RAG, tools, agents, and data boundaries — scope assets, generate attacks, score impact, fix, and retest in CI.
Software Supply Chain Security: Dependencies, Builds, and Releases
Secure the software supply chain from dependency selection through release — lockfiles, provenance, least privilege, secret controls, SBOMs, and response.