prompt-guard

Detect prompt injection attacks by screening user inputs and third-party data for malicious instructions designed to manipulate LLM behavior. This Skill uses model inference to identify and filter these injection attempts automatically.

Filter third-party data for jailbreak attempts by validating external inputs for embedded instructions and safety bypass threats. This approach prevents malicious payloads from overriding your LLM application constraints and safety filters.

Yes, you need transformers and torch installed for prompt injection detection. These dependencies provide the required model inference capabilities to classify inputs and identify malicious prompts within your application.

Yes, you can validate user inputs for a chatbot application to prevent harmful or malicious prompts from being processed. It checks user messages and third-party data for security threats, ensuring a safe chatbot experience.

Using model inference for LLM security filtering requires torch and transformers dependencies, adding computational overhead. It is best suited for security-sensitive applications where detecting jailbreaks and malicious prompts justifies the inference cost.