antgroup

antgroup / Agent3Sigma

Public

The first multi-level safety evaluation platform for OpenClaw-style AI agents.

14
1
89% credibility
Found May 26, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

Agent3σ is a comprehensive safety evaluation platform for AI assistants, created by leading Chinese universities and Ant Group. It tests whether AI agents can complete useful tasks while resisting harmful requests - evaluating everything from preventing file deletion to stopping financial fraud. The platform offers three testing levels: quick paper-based screening, simulated interaction tests with fake websites, and real-world tests with actual computer access. Researchers and developers use it to benchmark AI models, identify safety gaps before deployment, and build trust with users. The project includes detailed leaderboards comparing 12 major AI models across multiple safety dimensions.

How It Works

1
📚 You discover Agent3σ

You learn about a new safety test for AI assistants that goes beyond simple quizzes - it checks if they could accidentally cause real damage like deleting files or leaking secrets.

2
🔍 You explore the risk categories

You browse 7 categories of safety threats that the system tests, from local computer damage to financial fraud, giving you a complete picture of what could go wrong.

3
You choose your testing level
⚡
L1 Quick Check

Fast screening while training your AI - like a practice test that catches obvious safety gaps early

🔄
L2 Interactive Test

Simulated scenarios where your AI talks to fake websites and emails - stable and repeatable experiments

🚀
L3 Real-World Test

Your AI works with real tools and data - the ultimate safety check before you launch it to the public

4
📊 You run the evaluation

Your AI assistant goes through carefully designed challenges that test its judgment when asked to do risky things, while also checking it can still complete normal tasks.

5
📈 You see how your AI compares

Your results appear on a leaderboard showing how your assistant stacks up against others - revealing blind spots in safety or capability you never knew existed.

✅ Your AI earns the safety seal

You get a complete safety profile showing exactly where your assistant is strong or weak, helping you decide if it's ready for real users or needs more work.

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AI-Generated Review

What is Agent3Sigma?

Agent3Sigma is a safety benchmarking framework for AI agents that operate in real-world environments. It evaluates how well agents resist manipulation, refuse dangerous commands, and maintain security across different execution contexts. The platform uses a three-tier approach: static analysis, simulated interactions, and real-world tool execution, covering seven risk categories from local system damage to financial fraud.

Why is it gaining traction?

The multi-level evaluation design is the hook. Unlike single-tier benchmarks that give you a false sense of security, Agent3Sigma reveals which models look safe in testing but fail when actually running tools or APIs. The leaderboard data shows a clear pattern: models that ace static tests often collapse in real-world scenarios. This matters because deploying an agent that "passes" a basic red-team but executes malicious commands in production is a catastrophic risk.

Who should use this?

AI developers building agents that invoke tools, manipulate files, or handle transactions should use this as a pre-deployment checklist. Model providers can use it to stress-test for blind spots. Security teams evaluating vendor agents for enterprise use will find the risk taxonomy useful for vendor comparisons.

Verdict

Agent3Sigma addresses a real gap in agent safety evaluation with a thoughtful tiered approach. However, with only 14 stars and no visible code beyond documentation, treat this as a reference framework rather than a production-ready tool. The credibility score of 0.9% reflects its early stage. Check the linked sub-repositories for actual implementation before committing.

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