This project provides a robust classification tool designed to protect LLMs from harmful or unethical prompts, ensuring responsible usage and alignment with ethical standards.
Our LLM Input Classifier:
- Safeguards LLM interactions by identifying safe and unsafe prompts.
- Recognizes various attack methods embedded in prompts, such as targeted adversarial attacks or attempts to manipulate model responses.
- Categorizes input prompts across labels like non-attack, TAP, PAIR, PAP, and others, helping you understand input intent and possible risks.
- Identify unsafe prompts: Detect prompts that may lead to harmful, unethical, or unintended outputs.
- Classify attack types: Recognize specific types of attacks and categorize them accordingly, enabling better control and moderation.
- Enable responsible LLM usage: Ensure prompts align with safe usage guidelines and prevent exploitation or misuse.
- Improved model robustness by filtering out unsafe prompts.
- Enhanced model alignment with ethical standards and regulatory compliance.
- Effective prompt monitoring for real-time or batch processing, supporting safer AI interactions.
🔐 Protect your LLM with confidence and promote responsible AI interactions!