  As the Windows OS stands out as one of the most targeted systems, the
PowerShell language has become a key tool for malicious actors and
cybersecurity professionals (e.g., for penetration testing). This work explores
an uncharted domain in AI code generation by automatically generating offensive
PowerShell code from natural language descriptions using Neural Machine
Translation (NMT). For training and evaluation purposes, we propose two novel
datasets with PowerShell code samples, one with manually curated descriptions
in natural language and another code-only dataset for reinforcing the training.
We present an extensive evaluation of state-of-the-art NMT models and analyze
the generated code both statically and dynamically. Results indicate that
tuning NMT using our dataset is effective at generating offensive PowerShell
code. Comparative analysis against the most widely used LLM service ChatGPT
reveals the specialized strengths of our fine-tuned models.
