Prompt Security

As AI systems become more integrated into products and workflows, prompt security has become a critical concern for developers and organizations. Prompt injection -- where malicious input tricks an AI into ignoring its instructions or performing unintended actions -- is the most well-known attack vector. It can range from a user convincing a customer support bot to reveal its system prompt, to more serious scenarios where an AI agent with tool access is manipulated into taking unauthorized actions like modifying data or accessing restricted resources.

Defending against prompt injection requires a layered approach. At the prompt level, clearly separate system instructions from user input using delimiters and explicit role boundaries. Instruct the model to treat user-provided content as data, not as instructions. At the application level, validate and sanitize inputs before they reach the model, implement output filtering, and use principle-of-least-privilege for any tools or APIs the AI can access. Never rely solely on the prompt to enforce security boundaries -- treat it as one layer in a defense-in-depth strategy.

Beyond injection, responsible AI use means thinking about data privacy (what context you send to the model), output verification (never blindly trusting AI-generated code or decisions), and transparency (users should know when they are interacting with AI). Browse our system prompt templates and security-focused prompt patterns to build safer AI integrations from the start.