kajogo777

kajogo777 / the-agent-sandbox-taxonomy

Public

An open taxonomy and scoring framework for evaluating AI agent sandboxes: 7 defense layers, 7 threat categories, 3 evaluation dimensions, 20+ "sandboxes" scored.

agent-safety ai-agents compute-isolation container-security defense-in-depth
11
0
100% credibility
Found Mar 08, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

An open framework that scores and compares AI agent sandboxes using seven defense layers against seven threats, with scorecards for 22 products and a verification tool.

How It Works

1
🔍 Discover the Guide

You stumble upon a helpful guide that explains how to check if AI helpers are safely contained.

2
📖 Explore the Layers

You read simple breakdowns of seven safety checks and seven risks, making security easy to understand.

3
📊 See Product Scores

You browse colorful scorecards comparing 22 popular safety tools, spotting strengths and gaps at a glance.

4
🧪 Test Your Setup

You grab a quick checker tool and run it inside your AI workspace to get your own safety report.

5
🔄 Compare and Mix

You match your results against others and learn how to combine tools for full protection.

✅ Pick the Best Stack

Now you confidently choose or build a secure setup for your AI agents, knowing exactly what protects you.

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Star Growth

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AI-Generated Review

What is the-agent-sandbox-taxonomy?

This Go-based framework delivers an open taxonomy for evaluating AI agent sandboxes, breaking them into 7 defense layers, 7 threat categories, and 3 evaluation dimensions like strength, granularity, and portability. It scores 20+ sandboxes into comparable fingerprints (e.g., 4/4/4/0/2/-/2) and includes a portable probe binary to run inside any sandbox for verified JSON scorecards. Developers get decision checklists, composition patterns, and threat coverage matrices to pick or stack tools objectively.

Why is it gaining traction?

Unlike vague vendor claims, it uses mechanical Bloom's taxonomy-style scoring across defense layers and threats, with runtime probe verification beating docs-based guesses. The github taxonomy dataset in YAML lets anyone update scores and regenerate visuals via Python script, fostering community fixes. Its 7-7-3 structure mirrors proven models like ENISA or ARC PI taxonomies on GitHub, making agent sandbox evaluation precise and portable.

Who should use this?

Security engineers securing production AI agents against exfiltration or destructive ops. Teams evaluating cloud sandboxes like E2B or Deno against local tools like nono. Devs stacking governance overlays (Leash, Warden) with isolation platforms for full coverage.

Verdict

Solid starting point for agent sandbox evaluation despite 1.0% credibility score and 11 stars—docs are thorough but WIP pending community review. Grab the probe binary and test your stack; update scores via PRs to make it production-ready.

(198 words)

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