RedShell fine-tunes LLMs on enhanced malicious PowerShell data to produce syntactically valid offensive code for pentesting, reporting over 90% validity, strong semantic match to references, and better edit-distance similarity than prior methods plus functional execution success.
Advances in Neural Infor- mation Processing Systems30, 5999–6009 (2017), https://proceedings.neurips.cc/ paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
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Towards Automated Pentesting with Large Language Models
RedShell fine-tunes LLMs on enhanced malicious PowerShell data to produce syntactically valid offensive code for pentesting, reporting over 90% validity, strong semantic match to references, and better edit-distance similarity than prior methods plus functional execution success.