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numbergroup / AgentGuard

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A+ Grade AI Agent Security Framework - Military-grade protection against prompt injection, command injection, and Unicode bypass attacks

13
1
100% credibility
Found Mar 06, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

AgentGuard is a security tool that scans messages to AI assistants for dangerous commands, trick prompts, and hidden attacks, then cleans them or blocks them to keep things safe.

How It Works

1
😱 Hear about AI hacks

You read about sneaky tricks that can trick AI helpers into doing dangerous things, like running bad commands.

2
🔍 Discover AgentGuard

You find this helpful protector tool made to keep AI safe from those tricks.

3
🛡️ Add it to your AI setup

With a few simple steps, you connect the protector to your AI helper so it watches everything.

4
📝 Paste a message to check

You copy any message or chat you want to test into the checker.

5
⚡ See the safety report

In seconds, it tells you if it's safe or spots the sneaky parts and cleans them up.

6
📊 Review your protection log

You check the records to see what threats it blocked, feeling in control.

🎉 AI stays safe forever

Now your AI helper works securely without worries, blocking bad stuff automatically.

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AI-Generated Review

What is AgentGuard?

AgentGuard is a Python security framework that shields AI agents from prompt injection, command injection, Unicode homoglyph bypasses, and social engineering tricks—like the Clinejection attack that hit 4,000 dev machines via GitHub issues. You get CLI tools (`agent-guard analyze`, `github-issue`, `sanitize`), a simple Python API for threat scoring and sanitization, plus plug-and-play integrations as an OpenClaw skill or Claude MCP server for runtime verification of AI agents. It blocks threats surgically while keeping legit content intact, all with zero external dependencies.

Why is it gaining traction?

Its 0.02ms analysis speed and 50k+ throughput per second make it invisible in production pipelines, unlike heavier ML-based guards. Devs love the GitHub issue screening tailored for Clinejection risks, rate limiting, and security logging—features that deliver agentguardian learning access control policies to govern AI agent behavior without setup hassle. Zero-deps stdlib design means instant deployment, hooking agentguard ai users tired of vuln supply chains.

Who should use this?

AI agent builders on OpenClaw or Claude who process untrusted input like GitHub issues or user prompts. Repo maintainers using github grade calculator tools or screening contributions. Devs in agentguard runtime verification needing fast, local protection before scaling to full agent guardian setups.

Verdict

Grab it for prototyping AI agents or GitHub workflows—solid docs, CLI, tests, and MIT license make it low-risk despite 13 stars and 1.0% credibility score signaling early-stage (think github grade 6 potential). Monitor for community growth before prod lockdown.

(198 words)

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