prompt-guard

To detect prompt injection and jailbreak attempts in LLM applications, you can use a lightweight 86M-parameter classifier that evaluates user inputs and API responses to identify malicious instructions.

Yes, you can filter malicious prompts across multilingual deployments using a classifier that supports 8 languages and processes inputs in under 2ms on GPU.

To secure RAG pipelines against unsafe third-party data, apply a content-filtering model to batch process retrieved documents and block prompt injections before they reach the LLM.

Prompt injection detection works seamlessly with HuggingFace deployments, allowing you to load the 86M-parameter model directly for fast inference and risk-scoring.

The best way to prevent jailbreaks in enterprise chatbots is to deploy a lightweight risk-scoring classifier that filters user prompts and blocks unsafe instructions before processing.

An 86M-parameter model for LLM security offers fast inference under 2ms on GPU but may lack the deep contextual understanding of larger models, requiring batch processing for complex RAG pipelines.