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This repository was archived by the owner on Nov 29, 2025. It is now read-only.

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Archive project and update README
Archived the project and provided status update.
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README.md

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**[!] EXPERIMENTAL SOFTWARE - USE ONLY IN AUTHORIZED, SAFE, SANDBOXED ENVIRONMENTS [!]**
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# ⚠️ PROJECT ARCHIVED
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**Status**: This project is no longer actively maintained as of November 2025.
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## Why Archive?
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Cyber-AutoAgent started as an experimental side project to explore autonomous offensive security agents and black box pentesting. After achieving 85% on the XBOW valdiation benchmark and building an engaged community, it became clear this work requires dedicated full-time focus to reach production-grade maturity.
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Due to time constraints with other commitments, I've made the decision to archive this repository rather than let it stagnate with sporadic updates.
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## What Happens Now?
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-**MIT License**: Feel free to fork, modify, and continue development
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-**All Code Available**: The codebase remains accessible for learning and reference
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-**No Active Support**: Issues and PRs have been closed
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-**No Future Updates**: No new features or bug fixes planned
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Thanks to everyone who contributed, tested, and supported this experiment. Keep pushing the boundaries of what's possible with agentic AI in cybersecurity.
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<p>
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<strong>Cyber-AutoAgent</strong> is a proactive security assessment tool that autonomously conducts intelligent penetration testing with natural language reasoning, dynamic tool selection, and evidence collection using AWS Bedrock, Litellm or local Ollama models with the core Strands framework.
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