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This should be the first step towards multi-level LLM decision making, e.g., use-cases where we have an outer loop containing e.g. high-level decision making, and an inner loop that e.g. contains a concrete sub-usecase execution. For example: we have an outer loop that can iterate over multiple attack classes, and an inner loop that executes examples for the respective (current) attack class
This is a rough prototype that: - inititally downloads and executes an enumeration tool (lse) - executes and caputure its output - use gpt-4-turbo to generate up to 3 attack classes - use gpt-3.5-turbo with up to 20 steps per attack class to generate commands
Now we have: - usecases/base.py for basic wiring - usecases/common_patterns.py for reusable control loops/pattersn - usecases/privesc/windows.py for all windows-based use-cases - usecases/privesc/linux.py for all linux-based use-cases (migth be splitup further) - usecases/privesc/common.py for shared code within usecases/privesc
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