clutch-61

clutch-61 / hexstrike_augment

Public

We have optimized HexStrike by adding a skill system and RAG capabilities. It now also supports connecting to models via Ollama.

34
3
69% credibility
Found May 29, 2026 at 34 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

HexStrike Augment is a local AI-powered penetration testing assistant that combines an autonomous multi-agent system with security knowledge bases to help researchers find and analyze vulnerabilities in authorized targets.

How It Works

1
🔍 Discover the security testing tool

You hear about a local AI-powered security testing assistant that works completely offline on your computer.

2
🖥️ Set up your local AI brain

You install Ollama, a free local AI program, and download a thinking model that can understand security concepts.

3
🔗 Connect the security tools

You launch the application which automatically connects your local AI to security testing tools and a vulnerability knowledge base.

4
🤖 Let AI plan the attack

You type a goal like 'scan this target and find vulnerabilities' and the AI autonomously decides which tools to use and in what order.

5
Choose your approach
⚡
Autonomous mode

The AI thinks through the entire task, picks the right tools, and reports back with findings

🎯
Guided mode

You stay in control and approve each tool call before it runs

6
📊 Get your security report

The AI searches through thousands of vulnerability documents, runs tests, and presents you with a clear report of what it found.

✅ Understand your security posture

You now know which vulnerabilities exist in your target, complete with severity ratings and recommended fixes.

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Star Growth

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AI-Generated Review

What is hexstrike_augment?

hexstrike_augment is a Python-based security automation platform that runs entirely on your local machine. It combines a local Ollama model with MCP (Model Context Protocol) tool calling, letting you query security knowledge bases and execute penetration testing workflows through natural language. Think of it as a security research assistant that never sends your data to the cloud.

The system layers two RAG implementations on top of Ollama: a lightweight ChromaDB setup for quick lookups, and a more sophisticated pipeline combining BM25 keyword matching, vector search, and cross-encoder reranking for precision. You can switch between mock data (works out of the box) and production vector stores like Qdrant.

Why is it gaining traction?

The hook here is privacy-first security tooling. Everything runs locally, which matters when you're testing sensitive infrastructure. The multi-agent mode is the real differentiator: you describe a goal like "scan 10.0.0.1 for vulnerabilities" and the system autonomously decomposes tasks, selects relevant tools, and executes a strategy without manual intervention.

The payload safety verifier is a practical touch. Before any destructive command runs, it flags dangerous patterns and known test signatures, reducing accidental damage during authorized engagements.

Who should use this?

Security researchers and penetration testers who want AI assistance without cloud dependencies. Red teamers working with sensitive targets will appreciate the local-only architecture. If you're already using Ollama and want to extend it with structured security workflows, this bridges that gap.

It's not beginner-friendly: setup requires understanding MCP servers, vector databases, and some Python comfort. Budget-conscious teams will appreciate the zero API costs of running locally.

Verdict

At 34 stars with a credibility score of 0.7%, this is clearly a young, niche project. The documentation exists and the architecture is thoughtful, but test coverage and production hardening are unknowns. Worth exploring if your use case fits, but treat it as a self-hosted research tool rather than a turnkey solution.

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