Across
- 1. To attack your own AI to find its holes (one word)
- 3. Abused credentials let an agent act beyond its scope (ASI03)
- 4. A confident fact an LLM just made up
- 7. Insurance for AI agents that lapses on compliance drift
- 8. The buried clause that says "not covered" — the AI gap incumbents dodge
- 9. Prompt trick that bypasses a model's safety rules
- 12. When the hidden system prompt spills to the user (LLM07)
- 13. A safety filter wrapped around a model's input and output
- 15. NIST AI RMF function that sets the risk culture
- 17. Tricking an agent into abusing its own connected ones (OWASP ASI02)
- 18. Planting false data in an agent's store to warp later decisions (ASI06)
- 19. US agency behind the AI Risk Management Framework
Down
- 2. Confident, wrong output a model propagates as fact (LLM09)
- 5. Corrupting training data or weights to plant bad behavior (LLM04)
- 6. Too much autonomy or permission handed to an LLM (LLM06)
- 10. MITRE's adversarial-ML attack knowledge base
- 11. A misaligned agent acting on its own hidden agenda (ASI10)
- 12. The legal hook for who pays when the AI causes harm
- 14. Hidden text in a prompt that hijacks the model's behavior (OWASP LLM01)
- 16. Body behind the Top 10 lists for LLM and agentic apps
