Andrea Di Sorbo

Evaluating LLM-generated malware: an end-to-end framework and measurement study

This work presents an end-to-end methodology and the first measurement-based study that systematically compares malware generated by censored and uncensored LLMs against 119 real-world malware samples from multiple families and categories, estimating how safety-alignment affects malicious capabilities.