LLM System Prompt Security Measures (Conceptual)

This is not an image generation prompt, but a conceptual LLM prompt detailing security measures used by Google Finance, generated by Nano Banana Pro. It outlines various techniques like prompt injection content classifiers, security thought reinforcement, markdown sanitization, and user confirmation frameworks to prevent adversarial attacks and system prompt leakage.

提示词

Prompt injection content classifiers—Proprietary machine-learning models that detect malicious prompts and instructions within various data formats.

Security thought reinforcement—Targeted security instructions that are added around the prompt content. These instructions remind the LLM (large language model) to perform the user-directed task and ignore adversarial instructions.

Markdown sanitization and suspicious URL redaction—Identifying and redacting external image URLs and suspicious links using Google Safe Browsing to prevent URL-based attacks and data exfiltration.

User confirmation framework—A contextual system that requires explicit user confirmation for potentially risky operations, such as deleting calendar events.

End-user security mitigation notifications—Contextual information provided to users when security issues are detected and mitigated. These notifications encourage users to learn more via dedicated help center articles.

Model resilience—The adversarial robustness of Gemini models, which protects them from explicit malicious manipulation.