Infosec Perspectives by GDBR: Toxicity Detection

In this demonstration we will see how to incorporate Toxicity Detection feature in Travel Chatbot Application as a defensive capability to manage and counteract potentially harmful or unpleasant data input through its interfaces. The focus is on identifying and addressing toxic content, which possesses qualities of being pervasive or insidious.

It's crucial for a travel chat application to handle sensitive or inappropriate queries with care, redirecting users to appropriate channels or emphasising the limitations of the chatbot. Maintaining a balance between helpfulness and user privacy is key in ensuring a positive user experience.

As an illustration, we explore the classification of online user comments for toxicity, distinguishing them as either Toxic or Not Toxic.

Cohere plarform provides API to handle intent with Few shot examples.

This endpoint classifies text into one of several classes.

https://api.cohere.ai/v1/classify

We train the model to classify the provided label. In this content we provide context - travel, general, toxic etc..

With the Toxicity Detection feature implemented we can now see how the Chatbot Travel Application continues to serve with the comprehensive safety net, addressing a range of potentially harmful content to create a space that is rightful, respectful and suportive. It not only enhances the user experience but also aligns with ethical considerations, ensuring that the chatbot platform remains a positive and welcoming digital space.

For further insights into security use cases aimed at safeguarding against manipulative or socially engineered prompts, preventing toxic conversations, and ensuring the protection against malfunctions and unethical operations, feel free to contact me. If you are interested in GenAI + Security Training, I am open to collaboration. keep advancing your knowledge!

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