Securing AI Agents in Production: Monitoring, Logging, and Alerting

Last year, a single, undetected prompt injection attack compromised an entire conversational AI platform, resulting in a $100,000 payout to the attacker, all because the developers overlooked a critical aspect of AI agent security. The Problem from transformers import AutoModelForSeq2SeqLM , AutoTokenizer class Chatbot : def __init__ ( self , model_name ): self . model = AutoModelForSeq2SeqLM . from_pretrained ( model_name ) self . tokenizer = AutoTokenizer . from_pretrained ( model_name ) def generate_response ( self , prompt ): inputs = self . tokenizer . encode_plus ( prompt , return_tensors = ' pt ' ) response = self . model . generate ( ** inputs , max_length = 100 ) return self . tokenizer . decode ( response [ 0 ], skip_special_tokens = True ) chatbot = Chatbot ( ' t5-small ' ) print ( chatbot . generate_response ( " Tell me a joke " )) In this example, an attacker could exploit the generate_response method by crafting a malicious prompt that injects unwanted behavior into the c