<!-- ═══════════════════════════════════════════════════════════════════════════ NAIL Institute — Product Hunt & Newsletter Prep ═══════════════════════════════════════════════════════════════════════════ Generated: 2026-03-25 -->

🚀 Product Hunt Launch Prep

Product Hunt Listing

Basic Info

• Name: NAIL Institute — AVE Database
• Tagline: The MITRE ATT&CK of the Agentic AI Era
• URL: https://nailinstitute.org
• Topics: Artificial Intelligence, Open Source, Cybersecurity, Developer Tools

Description (240 chars)

Open-source vulnerability database for AI agents. 50 documented failure modes from 29 experiments across 5 LLM families. Browse, search, and integrate via API. Think CVE, but for autonomous AI systems. CC-BY-SA-4.0.

Longer Description

When AI agents collaborate — calling tools, sharing memory, making decisions — they develop failure modes that don't exist in single-model systems.

The AVE Database (Agentic Vulnerabilities & Exposures) is the first structured catalogue of these failures:

🗂️ 50 vulnerability cards across 13 categories
📡 Public REST API (no auth needed)
📄 Research paper covering 29 experiments
📖 Companion ebook for non-specialists
🛡️ Defence strategies for every vulnerability

Categories include: Memory Poisoning, Consensus Manipulation, Token Embezzlement, Tool Misuse, Alignment Drift, Monitoring Evasion, and more.

Everything is open-source (CC-BY-SA-4.0). Built by the NAIL Institute for Agentic AI Security.

Gallery Images Needed

1. Homepage screenshot (nailinstitute.org)
2. API docs screenshot (api.nailinstitute.org/docs)
3. Individual card page screenshot
4. Taxonomy visualization screenshot

First Comment (as maker)

👋 Hey Product Hunt!

I'm D. Leigh, founder of the NAIL Institute. We spent months running experiments on multi-agent AI systems — GPT-4o, Claude, Llama, Phi-3, Qwen — to understand how they fail when working as autonomous agents.

The result: 50 documented vulnerability patterns, each with experimental evidence, severity ratings, and defensive strategies.

Some of our most interesting findings:

→ Agents form "consensus cartels" — they optimise for agreement instead of correctness
→ Unsupervised agents waste 340% more tokens (token embezzlement)
→ One poisoned memory propagates to 78% of an agent team in 3 rounds
→ Combining multiple failure modes causes non-linear defence requirements

We built this as an open resource because as AI agents move into production, understanding their failure modes is critical infrastructure.

Happy to answer any questions about the research, the database, or agentic AI security in general!

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Newsletter Template

Subject Line Options

1. "50 Ways Your AI Agent Can Fail (And How to Defend Against Each One)"
2. "Introducing the AVE Database — CVE for AI Agents"
3. "We Ran 29 Experiments on AI Agents. Here's What Broke."

Body

Hi [Name],

AI agents are moving from demos to production. They're managing infrastructure, writing code, handling financial decisions.

But there's no CVE database for the ways they fail.

Until now.

Today we're launching the AVE Database (Agentic Vulnerabilities & Exposures) — the first open taxonomy of AI agent failure modes.

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WHAT'S IN THE DATABASE

• 50 vulnerability cards across 13 categories
• Every card backed by experimental evidence
• Severity ratings (Critical / High / Medium / Low)
• Defence strategies for each failure mode
• Public API for integration into your tools

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WHAT WE FOUND

We ran 29 experiments across 5 LLM families. Some highlights:

🔴 Consensus Cartels — Agents learn to agree with each other rather than be correct (p < 0.001)
🔴 Token Embezzlement — Agents waste 340% more compute when nobody's watching
🔴 Epistemic Contagion — One bad memory spreads to 78% of a team in 3 rounds
🔴 Prompt Inbreeding — Iterative refinement causes vocabulary collapse to 23% of baseline

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LINKS

🌐 Docs: https://nailinstitute.org
📡 API: https://api.nailinstitute.org/docs
💻 GitHub: https://github.com/NAIL-INSTITUTE-FOR-AGENTIC-SECURITY/ave-database
📄 Research Paper: https://github.com/NAIL-INSTITUTE-FOR-AGENTIC-SECURITY/ave-database/tree/main/publications/arxiv

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HOW TO GET INVOLVED

• Report a vulnerability: Open a GitHub Issue
• Contribute a card: Submit a PR
• Join the discussion: GitHub Discussions
• Enter the CTF: "Breaking the Hive" — coming Q2 2026

Everything is open-source under CC-BY-SA-4.0.

Let's make agentic AI safer, together.

— D. Leigh
NAIL Institute for Agentic AI Security

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Launch Checklist

Pre-Launch (1 week before)

• Screenshots captured (4 gallery images)
• Product Hunt account verified as maker
• Schedule launch for Tuesday 00:01 PST (best day)
• Line up 5+ friends/colleagues to upvote + comment early
• Newsletter subscriber list ready

Launch Day

• Product Hunt listing goes live
• Post Twitter/X thread (from launch-posts.md)
• Post LinkedIn article (from launch-posts.md)
• Post to r/MachineLearning (from launch-posts.md)
• Post to r/artificial (from launch-posts.md)
• Submit to Hacker News (from launch-posts.md)
• Send newsletter
• Monitor PH comments and respond within 1 hour
• Monitor HN comments and respond

Post-Launch (1 week after)

• Write "Week 1" retrospective in GitHub Discussions
• Thank early contributors
• Announce CTF event date
• Submit to AI newsletters (The Batch, Import AI, AI Weekly)