YutoTerashima

YutoTerashima / mcp-tool-security-playground

Public

MCP-style tool-use security playground with permission policies.

ai-agents mcp prompt-injection security tool-use
11
0
100% credibility
Found May 03, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A research playground for testing security policies in AI agents that use tools, featuring mock tools, policy decisions, prompt injection experiments, and automated report generation.

How It Works

1
🔍 Discover the playground

You find this GitHub project while exploring ways to make AI assistants safer when using everyday tools like calculators or file readers.

2
📖 Read the guide

You skim the welcoming notes to learn it's a safe testing ground for deciding which tool uses are okay, need review, or should be blocked.

3
🚀 Set it up easily

You follow simple steps to prepare everything on your computer so you can start testing right away.

4
✨ Watch security in action

You run a quick demo and see examples like 'allow simple math' or 'deny risky file access' pop up, showing how protections work.

5
🧪 Run safety tests

You launch experiments with real-world examples to check how well the safeguards spot tricky prompts trying to misuse tools.

6
📊 Review your findings

You check colorful charts, summaries, and logs that explain what worked, what failed, and why.

🎉 Master tool safety

You now understand better ways to keep AI agents secure, like using smart rules and checks instead of just trusting prompts.

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Star Growth

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AI-Generated Review

What is mcp-tool-security-playground?

This Python playground lets you experiment with MCP-style tool-use security, enforcing permission policies on agent tool calls like file reads or network posts. It simulates prompt-injection attacks, audits decisions with risk scores, and runs reproducible GPU benchmarks on real datasets to compare static policies against TF-IDF detectors. Developers get quick demos, policy routing, and auto-generated reports with metrics, figures, and failure analysis.

Why is it gaining traction?

It stands out with ready-to-run experiments on 15k+ prompt-injection samples, showing hybrid detectors hit 0.96 macro-F1 while exposing allow/deny tradeoffs. The GPU smoke tests, ablation probes for perturbations like hidden instructions, and redacted audit logs make security testing concrete without building from scratch. Python scripts handle data download, processing, and reporting in one command.

Who should use this?

AI agent builders securing MCP tool integrations against injection risks. Security researchers benchmarking policy layers vs classifiers on public datasets. Teams prototyping human-review queues or path allowlists before production.

Verdict

Worth forking for research prototyping—reproduce V2 results in minutes despite 11 stars and 1.0% credibility signaling early maturity. Solid docs and pytest coverage, but scale experiments cautiously until more adoption.

(198 words)

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