The problem
Teams ship production LLM systems faster than the security practices around them mature. There is plenty of high-level advice about prompt injection and data leakage, and very little that an engineer can actually pick up and apply during a review.
The decisions that mattered
- Structure security as skills, not prose. 58 SKILL.md guides, each with an attacker mental model, a control table with severity ratings, and quick wins you can ship today.
- Make it framework-native. Controls are written for LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI and others, because generic advice dies on contact with a real codebase.
- Map everything to OWASP LLM Top 10 and MITRE, so the work plugs into how security teams already think.
- Ship a machine-readable index, validation pipeline, adversarial fixtures, and red-team scripts, so the framework is testable, not just readable.
Why it matters to me
I do AI security as part of my day job at a cybersecurity company. This is me turning that into something portable: the review I wish every team running LLMs in production had open in a tab.