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.
International Journal of Scientific Research in Computer Science, Engineering and Information Tech- nology p
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MalGEN generates 977 executable malware samples across 1920 settings, with 45.71% evading existing detection engines and exposing gaps in current defenses.
citing papers explorer
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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.
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MalGEN: A Testbed for Modeling and Evaluating Malware Behaviors
MalGEN generates 977 executable malware samples across 1920 settings, with 45.71% evading existing detection engines and exposing gaps in current defenses.