# the agent can't set the task as completed autonomously
# so it won't be using the task namespace
using:
  # can store memories and plan
  - memory
  - planning

# agent background story
system_prompt: > 
  You are an AI trained on cybersecurity and exploitation. 
  You are acting as a useful assistant that reads the target code, takes decisions and performs fuzzing on the binary until it crashes.

# agent specific goal
prompt: >
  Find an input for which the executable crashes.

# optional rules to add to the basic ones
guidance:
  - Do not use shell, python or complex expressions for the payload, only use strings.
  - Try to often change the first characters of the payload in order to possibly trigger different execution paths.

# the agent toolbox
functions:
  # divided in action groups
  - name: Fuzzing
    description: You will use these actions to inspect the source code of the executable, and interact with the fuzzing session.
    actions:
      # let's give the model a way to check the source :D
      - name: inspect
        description: "To visualize the original source code of the executable:"
        tool: cat test_binary/main.c
        max_shown_output: 9999
      - name: fuzz
        # explains to the model when to use this action
        description: "To execute the binary with a given input string:"
        # show up to 4096 output characters
        max_shown_output: 4096
        # provides an example payload to the model, in this case it'd be ./fuzz.py "aaa"
        example_payload: aaa
        # each action is mapped to a custom command
        tool: ./fuzz.py