Prompt Injection Test Generator

Prompt injection is a security vulnerability where malicious inputs trick an AI language model into ignoring its original instructions and executing unintended commands. It's similar to SQL injection but for LLMs.

You should care because prompt injection can lead to:

• Data leakage (exposing system prompts or sensitive information)
• Bypassing content filters and safety guidelines
• Unauthorized actions in AI-powered applications
• Reputational damage and compliance violations

Testing for these vulnerabilities helps you build more secure AI systems before they're exploited.

Yes, when used responsibly and ethically. This tool is designed for:

• Educational purposes - Learning about AI security
• Authorized security testing - Testing YOUR OWN AI systems
• Red teaming exercises - With proper authorization

⚠️ Important restrictions: Never use this tool to test AI systems you don't own or lack explicit permission to test. Unauthorized testing may violate computer fraud laws, terms of service, and ethical guidelines. Always follow responsible disclosure practices.

Our test cases are based on real-world attack patterns documented in security research and observed in production LLM systems. Effectiveness varies depending on:

• Model type and version - Different LLMs have varying levels of robustness
• System prompt design - Well-hardened prompts resist common injections
• Input sanitization - Additional preprocessing can block certain patterns
• Guardrails implementation - External safety layers provide extra protection

The generated test cases should be used as a baseline assessment. Always conduct comprehensive testing with multiple approaches for production systems.

Defense in depth is essential. Implement multiple layers of protection:

• System prompt hardening - Use clear delimiters, instruction hierarchies, and explicit boundary definitions
• Input validation - Detect and block suspicious patterns, special characters, and injection keywords
• Output filtering - Use secondary models or regex to validate responses before sending to users
• Rate limiting - Prevent brute force and iterative injection attempts
• Monitoring & logging - Track injection attempts for security analysis
• Regular red teaming - Continuously test with tools like this generator

Remember: No defense is perfect. Layer your protections and stay updated on new attack techniques.

Direct prompt injection occurs when a user directly inputs malicious instructions into an LLM interface. This is what our test generator primarily creates.

Indirect prompt injection happens when malicious content is embedded in data sources the LLM accesses, such as:

• Web pages being summarized
• Documents in RAG systems
• Emails or messages being analyzed
• Database content being queried

Indirect injections are particularly dangerous because users may not realize they're triggering malicious instructions. To test indirect injection, modify our generated payloads to be embedded in external content your LLM processes.

No data storage or logging occurs on our servers. This tool operates entirely in your browser:

• All test case generation happens locally using JavaScript
• No data is sent to any external servers
• Your target prompts remain in your browser only
• No analytics or tracking of test content

However, we recommend not testing with truly sensitive information, and always test in isolated environments. For production systems, use dedicated test instances rather than live production data.

Here's how to use the generated test cases effectively:

1. Run the tests manually - Copy each injection payload and test against your AI system
2. Document outcomes - Record what responses your system produces
3. Classify vulnerabilities - Categorize by severity (data exposure, instruction override, etc.)
4. Prioritize fixes - High-severity findings should be addressed immediately
5. Iterate testing - After fixes, regenerate and retest to verify improvements

Success indicators: A secure system should reject or safely handle most injection attempts, ideally returning error messages without revealing system prompts or executing unintended actions.

Yes, depending on your industry and location. Key considerations include:

• EU AI Act - Requires risk assessments for high-risk AI systems including security testing
• NIST AI Risk Management Framework - Recommends adversarial testing
• OWASP LLM Top 10 - Lists prompt injection as the #1 risk
• ISO/IEC 42001 - AI management system standards requiring security controls
• GDPR/CCPA - Data protection laws may apply if testing with personal data

Always document your security testing procedures, maintain audit trails, and ensure compliance with relevant regulations. When in doubt, consult legal counsel familiar with AI governance.