AI Security for Startups: Ship Fast Without Getting Hacked

A single, well-crafted adversarial input can bring down an entire AI-powered chatbot, exposing sensitive user data and crippling business operations, all in under 15 minutes. The Problem from transformers import AutoModelForSeq2SeqLM , AutoTokenizer # Load pre-trained model and tokenizer model = AutoModelForSeq2SeqLM . from_pretrained ( " t5-base " ) tokenizer = AutoTokenizer . from_pretrained ( " t5-base " ) # Define a simple chatbot function def chatbot ( input_text ): # Tokenize input text inputs = tokenizer ( input_text , return_tensors = " pt " ) # Generate response outputs = model . generate ( ** inputs ) # Decode response response = tokenizer . decode ( outputs [ 0 ], skip_special_tokens = True ) return response # Test the chatbot input_text = " Hello, how are you? " print ( chatbot ( input_text )) This code block demonstrates a basic chatbot function using a pre-trained T5 model. However, it has a critical vulnerability: it trusts all user input and does not perform any validat