Interpretable Detection and Threat Characterization of Malicious PowerShell Scripts via Multi-Level Representation Fusion and LLM Reasoning

Researchers developed a method that uses LLMs (large language models, AI systems trained on vast text data) combined with multiple levels of code analysis to detect and understand malicious PowerShell scripts (code designed to harm Windows systems). The approach fuses different representations of the scripts and applies LLM reasoning to characterize threats in an interpretable way, meaning users can understand why the system flagged something as dangerous.