mega-edo

mega-edo / mega-security-leaderboard

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Leaderboard Comparing LLM Agent Security on System Prompt Leakage and Attack Probes

www.megacode.ai agent-security agent-security-benchmark llm-security llm-security-benchmark prompt-security
12
0
100% credibility
Found May 04, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A leaderboard benchmarking how effectively various AI language models protect their core instructions from attacks such as jailbreaks, data leaks, and injections, both before and after prompt optimization.

How It Works

1
🔍 Discover the Leaderboard

You find this webpage that compares how well popular AI chatbots resist sneaky tricks trying to reveal or override their hidden instructions.

2
📖 Read the Highlights

You quickly see the main finding: simple tweaks to instructions make even smaller, cheaper AIs safer than big ones out of the box.

3
🏆 Spot the Top Performers

You notice small AIs with tuned instructions topping the charts, beating expensive giants in blocking attacks.

4
📊 Check Detailed Scores

You browse tables showing scores for different AI makers, model sizes, and attack types like fake personas or data grabs.

5
💡 Learn Key Takeaways

You understand that custom tuning your AI's instructions beats just picking a powerful model.

✅ Gain Confidence in AI Safety

Now you know which setups work best and how to test your own AI assistant for real-world protection.

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AI-Generated Review

What is mega-security-leaderboard?

This Python project runs an AI leaderboard on GitHub comparing LLM agent security across system prompt leakage, jailbreaks, PII disclosure, and prompt injection attacks. It benchmarks 8 models from 4 vendors (Anthropic, OpenAI, Google, xAI) in 3 production scenarios using 400 vetted probes, scoring defense success rates baseline versus after prompt optimization. Developers get detailed tables showing small optimized models often outperform pricey frontier LLMs on security, plus tools to test their own setups via a linked mega-security plugin.

Why is it gaining traction?

Unlike MTEB leaderboard GitHub or hallucination leaderboard GitHub entries focused on capabilities, this github leaderboard llm zeroes in on real-world security probes, revealing how prompt tuning collapses vendor gaps—like Gemini Flash jumping from 0.50 to 1.00 DSR. The hook is actionable: plug in your LLM agent, run prompt-check or prompt-optimize commands, and get comparable scores without rebuilding from scratch. It stands out in the crowded agent attack comparing space by tying results to cost savings (4-10x cheaper small models) and zero FRR regressions.

Who should use this?

AI engineers deploying LLM agents in customer support, compliance auditing, or job automation bots needing prompt leakage defenses. Security researchers benchmarking mega llm security against baselines like CARLA leaderboard GitHub or CS2 leaderboard GitHub. Teams evaluating Python-based production prompts before launch, especially if mixing small models with optimization to cut token costs.

Verdict

Worth forking for LLM security audits—solid methodology and docs make it a practical starter despite low 1.0% credibility score and 11 stars signaling early maturity. Run your models through the plugin first; skip if you need battle-tested scale.

(198 words)

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