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Always sanitize user inputs before passing them to LLMs. Use input validation, output filtering, and privilege separation to prevent prompt injection attacks that could manipulate your AI system into performing unintended actions.

Never execute LLM-generated code in a privileged environment without sandboxing. Use containers, restricted runtimes, or code review gates to prevent generated code from accessing filesystems, networks, or databases it should not touch.

Consider using open-source models for internal tools where data privacy is a concern.

Challenge: Present an LLM with a classic ethical dilemma (e.g., the Trolley Problem or a scenario involving privacy vs. security). Ask it to argue for both sides of the dilemma, using different ethical frameworks (like utilitarianism and deontology).

Never blindly trust code generated by an LLM. Always thoroughly test it for correctness, security vulnerabilities, and performance issues before using it in production.

Do not send personally identifiable information (PII) or other sensitive data to public LLM APIs unless you understand their data usage policies and have appropriate safeguards.

If LLM outputs are displayed to users or used in other systems, always validate and sanitize them to prevent injection attacks or the display of inappropriate content.

If incorporating user input into prompts, be aware of prompt injection vulnerabilities. Sanitize user inputs or use techniques to separate instructions from user data.

Never embed API keys directly in client-side code or public repositories. Use environment variables or secure secret management services.

Ask LLMs to review code for potential security vulnerabilities, but always follow up with human security experts for critical applications.