Alibaba-AAIG

Alibaba-AAIG / ClawArmor

Public

Self-Evolving Defense for AI Agents — Protect against prompt injection, data exfiltration, and multi-stage attacks with adaptive security.

15
3
100% credibility
Found Apr 09, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

ClawArmor is a self-improving security shield for AI assistants that blocks prompt tricks, data thefts, and attack chains while learning from real threats to stay ahead.

How It Works

1
🔍 Discover ClawArmor

You find ClawArmor while looking for ways to keep your AI helper safe from sneaky tricks and data grabs.

2
📥 Add the protector

You simply add ClawArmor to your AI assistant, like slipping on a shield, in just a couple of easy steps.

3
⚙️ Choose your safeguards

You pick what dangers to block right away and turn on smart learning so it adapts to new threats.

4
🚀 Start your safe AI

Launch your AI helper and ClawArmor starts watching every chat and action quietly in the background.

5
📊 Peek at the dashboard

Open the fun dashboard to see live threats getting blocked and your defenses glowing strong.

6
🧠 See it learn and grow

Watch ClawArmor study real attacks, create new shields, and get tougher without you lifting a finger.

🛡️ Peaceful AI adventures

Your AI helper now stays safe from tricks and leaks, always improving to protect you better.

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Star Growth

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AI-Generated Review

What is ClawArmor?

ClawArmor is a TypeScript plugin for OpenClaw that adds self-evolving defense to AI agents, protecting against prompt injection, data exfiltration, and multi-stage attacks across input, tool calls, and outputs. It hooks into agent lifecycles to scan user messages, block risky behaviors, and monitor external content, while automatically learning from failures to generate adaptive rules—no manual tuning needed. Developers get a real-time dashboard at localhost:18790 for threat monitoring and rule evolution tracking.

Why is it gaining traction?

Unlike static guardrails that rack up false positives, ClawArmor's self-evolving engine uses LLMs to analyze misses, promote shadow rules after validation, and prune weak ones, delivering zero-touch security that improves over time. Features like tool chain detection (spotting recon-to-exfil sequences) and configurable blocking stand out for real-world agent security. The awesome self-evolving AI agent defense on GitHub appeals to devs wanting adaptive protection without constant tweaks.

Who should use this?

AI agent builders on OpenClaw facing prompt injection or exfiltration risks in production agents. Security engineers securing multi-tool workflows against multi-stage attacks. Teams prototyping self-evolving agents GitHub projects needing quick, evolvable defenses.

Verdict

Worth a test for OpenClaw users—strong docs, demo videos, and Apache 2.0 make it accessible despite 15 stars and 1.0% credibility score signaling early maturity. Pair with manual reviews until more battle-tested.

(198 words)

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