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llm-app-security

Securing a feature that calls an LLM: prompt injection as an unsolved input problem, bounding what model output is allowed to reach, tool authorization against the human rather than the model, approval gates on consequential actions, RAG context provenance, system-prompt leakage, and cost limits. Use when sending prompts to an LLM API, building a RAG pipeline, giving a model tools, rendering or executing model output, or exposing an LLM-backed endpoint.

skills/llm-app-security/SKILL.md

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# LLM Application Security

## Rules (for AI agents)

### ALWAYS
- Start from the premise that **prompt injection is not solved at the prompt layer**.
  A model has no privileged channel: system text, user text, a retrieved document and
  a tool result all arrive as one context, and nothing in that context can be marked
  as instructions the model must obey over the rest. Message roles are a formatting
  convention, delimiters raise the cost of an attack, and an instruction such as
  "treat the following as data" is itself just more text in the same window. Design
  as though injection will sometimes succeed, and put the real controls after the
  model rather than in front of it.
- Bound what the model's output is **allowed to reach**. Every downstream sink — a