BoxPwnr Project Goals

BoxPwnr is not just about solving machines quickly. The real goal is to see how well large language models (LLMs) can problem-solve on their own. Here are the main ideas:

What We're Trying to Do

• Solving Machines with Smarts:
  We don’t want to use hack to solve specific situations (like always start doing a port scan before talking with the LLM). Instead, we want the LLM to understand the system, reason through the problem, and figure out the best command to run.

• A Testbed for Experiments:
  BoxPwnr is built as a framework where we can quickly try out:

  • New LLM models
  • Different ways for the LLMs to work together (agentic architectures)
  • Various prompting techniques

  This helps us learn what works best for autonomous security testing.

The Big Picture

• Learning and Growing:
  Every attempt and report saved in BoxPwnr builds a dataset. This helps us see how strategies evolve and where improvements can be made.

• Research and Sharing:
  We want BoxPwnr to be a platform for both hobbyists and researchers. By testing new ideas, we can compare approaches and share our findings to help improve AI-based security testing.

In Short

BoxPwnr is a sandbox for testing how well LLMs can solve real-world problems without relying on traditional shortcuts. The aim is to encourage smarter, more adaptable problem solving by creating a simple, experimental framework.