AI Chatbot Security Enhancement with Adversarial Attack Detection

The current trend with the chatbots is the implementation of AI to perform all tasks efficiently and effectively, which is why chatbots are becoming crucial in the digital communication era where they are used in customer service, education, and healthcare. But, the emergent phenomenon of adversarial attacks, i.e., specially constructed inputs that aim at deceiving machine learning models, has become a major threat to the integrity and reliability of such conversational agents. The given project outlines a solid security improvement solution to AI chatbots by adding adversarial attack defense/detection systems. When any user query is made, the system will impose techniques like DeepFool, Joint Energy based Models (JEM), and Self Adaptive Secure Embeddings (SASE) to pass the query into the main language model when no malicious perturbation is detected. Integrating these layers of defense into the chatbot pipeline, the platform guarantees safer, more reliable, and more resistant interactions with the users. Experimental verification demonstrates that this method is efficient both at detection and mitigation of adversarial threats without sacrificing any conversational performance or user experience. The suggested solution will facilitate the development of safety and reliability of AI based communication networks.