mattijsmoens

mattijsmoens / intentshield

Public

Pre-execution intent verification for AI agents. Audits what your AI is about to do, not what it says. Zero dependencies, deterministic, hash-sealed.

www.linkedin.cominmattijs-m-b48b95245 ai-agents ai-safety ai-security autonomous-agents cybersecurity
17
3
100% credibility
Found Mar 11, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

IntentShield is a lightweight Python tool that reviews and blocks risky actions proposed by AI assistants before they execute, such as dangerous commands or file access.

How It Works

1
🔍 Discover IntentShield

While building an AI helper, you find this safety shield that checks what your AI wants to do before it happens.

2
📥 Add the shield

You easily bring the safety tool into your AI project with a simple download.

3
🛡️ Choose safe actions

You list the helpful things your AI can do, like searching the web or writing notes, so it stays in bounds.

4
🔒 Turn it on

You activate the protector, and it seals itself to prevent any sneaky changes.

5
🧪 Try it with examples

You test ideas from your AI, seeing safe ones go through while bad ones like deleting files get stopped.

6
🚀 Let your AI work safely

Your AI now thinks step-by-step, proposing actions that the shield approves instantly.

🎉 Peace of mind

Your AI helper runs reliably all day, protected from tricks, mistakes, or harm, just as you wanted.

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AI-Generated Review

What is intentshield?

IntentShield is a Python library for pre-execution intent verification in AI agents. It audits proposed actions—like shell commands, file writes, or URLs—before execution, focusing on what the agent plans to do rather than what it says. With zero dependencies and deterministic, hash-sealed checks, it slots into agent pipelines via simple API calls like `audit()` or `audit_parsed()`.

Why is it gaining traction?

It stands out by blocking attacks that slip past output filters, such as jailbreaks, injections, and exfiltration, using fast regex rules without ML latency or external APIs. Developers hook it for its tamper-proof hash-sealing and production-tested reliability in 24/7 agents. Zero dependencies eliminate supply chain risks, making it a no-brainer pip install.

Who should use this?

Agent builders integrating tools like search, browsing, or file ops into LLMs, especially backend devs running autonomous systems for trading, research, or automation. Ideal for Python teams needing lightweight safety layers without added deps or overhead.

Verdict

Worth prototyping for agent verification—strong docs, demo script, and 53 tests show polish despite 14 stars and 1.0% credibility score. Early maturity means validate in your stack, but BSL license fits non-prod experiments.

(178 words)

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