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.
Advances in Neural Information Pro- cessing Systems30, 5999–6009 (2017).https://proceedings.neurips.cc/paper_ files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
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RedShell: A Generative AI-Based Approach to Ethical Hacking
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.