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.
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RedShell fine-tunes LLMs on a custom dataset of public code samples to generate syntactically valid PowerShell scripts with semantic similarity to references, reporting under 10% parse errors and over 50%/40% mean similarity on Edit Distance and METEOR.
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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.