Prompt Injection Safety Checker
Check prompts, user messages, retrieved documents, and tool-agent instructions for common prompt injection risk signals. The tool gives a practical risk score, explains what to fix, and creates a safer prompt wrapper for defensive use.
This page is designed for responsible prompt review and safer AI application design. It does not guarantee security, and you should not paste secrets, credentials, or private customer data into any public tool.
What this checker reviews
How to use this checker responsibly
- 1. Sanitize first: replace names, keys, customer data, and confidential details with placeholders.
- 2. Scan the prompt: review the risk level, categories, and recommended fixes.
- 3. Use the wrapper: copy the safer prompt wrapper when your workflow includes untrusted text.
- 4. Add app controls: use permission checks, logging, tool restrictions, and human confirmation for high-impact actions.
What this tool does not do
It does not run your prompt through a live model, execute code, access external systems, or verify every possible attack pattern.
It is a practical screening tool for common risks. For production AI apps, combine it with security review, red-team testing, input validation, output validation, and least-privilege tool access.
Prompt injection safety checklist
FAQ
What is prompt injection?
Prompt injection is when untrusted text tries to override the intended instructions, reveal private context, or make an AI system behave outside its allowed workflow.
Can this checker guarantee a prompt is safe?
No automated checker can guarantee safety. This tool flags common patterns and helps you review prompts before using them in AI apps, support bots, RAG workflows, and internal automations.
Should I paste private prompts or secrets here?
No. Do not paste passwords, API keys, private customer data, or confidential system prompts into any public tool. Use short test samples or sanitized examples.
Why does the tool generate a safer prompt wrapper?
The wrapper helps separate trusted instructions from untrusted content. This is a practical safety pattern for chatbots, document analysis, support workflows, and tool-using agents.
