A cognitive runtime for black-box penetration testing on Claude Code that preserves world state and minimizes early closure, rather than acting as a scanner or expert system.

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License

MIT

Last updated

2026-07-30

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FR-AI / ANALYSIS

Why it is worth attention

It uniquely addresses the problem of LLMs prematurely compressing the world by focusing on state preservation, anti-compression discipline, and model-driven experimentation, rather than following rigid workflows or vulnerability menus.

Who it is for

  • Penetration testers and security researchers
  • AI agent developers exploring cognitive architectures
  • Bug bounty hunters conducting manual or semi-automated testing
  • Red team operators seeking flexible, state-aware tooling

Use cases

  • Performing authorized black-box penetration tests with an AI agent
  • Running experimental hypotheses and recording evidence for targeted assessments
  • Building and maintaining a structured world model during complex multi-step attacks
  • Generating structured reports for vulnerability disclosure (e.g., bug bounty platforms)

Strengths

  • Object- and evidence-centered world model that resists premature compression
  • Minimal runtime that only provides capabilities and projections, leaving decisions to the model
  • First-class experimental artifacts with differential audit trails
  • Clear separation of concerns (contract, knowledge, runtime, artifacts) and cold knowledge loading

Considerations

  • Requires Claude Code CLI and Python 3.8+, no standalone deployment
  • Only intended for authorized targets; misuse is the user's responsibility
  • Steep learning curve for understanding its cognitive model and anti-compression principles
  • Dependent on LLM reasoning quality and may not guarantee finding vulnerabilities

README quick start

使用

Description

An experimental cognitive runtime for exploring object-centered autonomous agents.

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