prompt-guard

Prompt injection detection works by classifying user input and third-party data to identify malicious instructions attempting to override system prompts. This filtering happens before the input reaches the LLM, preventing unauthorized command execution.

Yes, multilingual jailbreak detection is supported across 8 different languages. The classifier maintains high true positive rates and low false positive rates, effectively flagging safety guideline overrides in non-English inputs.

Yes, you can deploy the classifier via HuggingFace transformers to scan for RAG security threats. It supports batch processing to filter malicious prompts embedded in retrieved third-party data before LLM generation.

Yes, you need PyTorch installed because the classifier depends on the torch and transformers libraries. These dependencies power the 86M parameter model used to evaluate input safety and detect jailbreak attempts.

Filtering jailbreak attempts is best handled by a dedicated lightweight classifier that evaluates inputs before LLM processing. This approach offers high true positive rates and low false positive rates across multiple languages.

You should use an automated safety alignment classifier when processing high-volume user queries or integrating untrusted third-party data. It efficiently scales jailbreak detection where manual review would create application latency.