Building a Security Test Suite for Your LLM Application

A single, well-crafted malicious input can bring down an entire LLM application, compromising user data and undermining trust in AI-powered services. The Problem from transformers import AutoModelForSeq2SeqLM , AutoTokenizer # Load pre-trained model and tokenizer model = AutoModelForSeq2SeqLM . from_pretrained ( " t5-small " ) tokenizer = AutoTokenizer . from_pretrained ( " t5-small " ) def generate_text ( input_text ): # Tokenize input text inputs = tokenizer ( input_text , return_tensors = " pt " ) # Generate output text outputs = model . generate ( ** inputs ) # Convert output to text output_text = tokenizer . decode ( outputs [ 0 ], skip_special_tokens = True ) return output_text # Test the function with a benign input print ( generate_text ( " Hello, how are you? " )) In this vulnerable example, an attacker can exploit the generate_text function by providing a carefully crafted input that manipulates the model into producing a malicious output. For instance, an attacker might inpu