Defend Against Prompt Injection Attacks: Secure Your Large Language Models

Prompt injection attacks can manipulate Large Language Models (LLMs) to perform unintended actions, potentially exposing sensitive data or causing harm. This article outlines three approaches to defend against such attacks, each with its own strengths and weaknesses.


  • Prompt injection attacks: Malicious user input can trick LLMs into executing harmful actions.
  • Risks: These attacks can steal data, inject malicious code, or disrupt system functionality.
  • Simple Prompt Guard: This basic approach instructs the LLM to reject suspicious prompts. Works well for simple applications but lacks flexibility.
  • Classifier Multi-Prompt: This approach uses multiple prompts, including one to classify user input and another to detect malicious intent. Offers more control but is slower and prone to misclassification.
  • Universal Safety: Every user input is run through a malicious prompt, ensuring safety but increasing processing time and false positives.
  • Choosing the right approach: Consider the application's complexity, data sensitivity, and performance requirements. Combining approaches can offer the best protection.

This article also introduces NexusGenAI, a platform that simplifies building secure LLM applications.


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