Practical AI Risk Education
The LLM Risk Atlas
A practical field guide to how AI systems fail in ways that look right — including hallucinations, drift, prompt injection, privacy leakage, tool misuse, overconfidence, and unsafe automation.
AI is powerful, but it can be wrong in ways that look right. The Atlas helps students, developers, cybersecurity analysts, business leaders, and risk teams recognize common failure modes and reduce the risk before acting on AI output.
Explore
Pitfall Explorer
Browse language model failure modes by category, including factuality, security, privacy, tool-use, context, and coding risks.
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Practical Checklists
Use checklists before trusting AI answers, pasting sensitive data, using AI-generated code, researching security issues, or deploying AI tools.
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Role Guides
See how the same AI risks affect students, developers, cybersecurity analysts, business leaders, and compliance or risk teams.
What the Atlas helps with
Slow down before AI confidence becomes human overtrust.
The LLM Risk Atlas is designed to help users ask better questions: Is this answer verified? Is the source real? Is the model using current information? Is sensitive data being exposed? Could a tool action cause harm? Should a human review this before acting?
For technical users
Focus on prompt injection, non-runnable code, version mismatch, insecure code, tool misuse, and security verification.
For decision-makers
Focus on overconfidence, automation bias, data leakage, vendor risk, policy gaps, and review boundaries.