Why Your AI Agent Trusts Too Much — And How to Fix It

A single, well-crafted prompt can bypass the entire security posture of an LLM-based AI agent, allowing attackers to extract sensitive information, manipulate user interactions, or even take control of the entire system. The Problem import transformers from transformers import pipeline # Initialize the LLM pipeline nlp = pipeline ( ' question-answering ' ) # Define a function to handle user input def handle_user_input ( user_input ): # Retrieve a document based on the user's query document = retrieve_document ( user_input ) # Use the LLM to answer the user's question answer = nlp ({ ' question ' : user_input , ' context ' : document }) # Return the answer to the user return answer # Define a function to retrieve a document def retrieve_document ( query ): # This function retrieves a document from a database or API # For simplicity, let's assume it returns a static document return " This is a sample document. " In this vulnerable code example, an attacker can craft a malicious prompt th