paolodm

paolodm / ai-coding-agents-incidents

Public

Structured corpus of real security incidents caused or amplified by AI coding agents

10
4
89% credibility
Found May 23, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This is a community knowledge base that tracks real incidents where AI coding tools like Claude Code, Cursor, and Codex caused problems. Think of it like a shared notebook where people write down what went wrong so others can learn from those mistakes. The repository contains a list of documented incidents in two formats (spreadsheet and JSON), and includes a small program that keeps both formats in sync. Anyone can browse the incidents to learn about potential risks before using these tools, or contribute their own experiences to help the community.

How It Works

1
🔍 You hear about AI coding tools causing problems

A friend mentions their AI assistant accidentally deleted important files or broke their project.

2
📚 You search for answers and find this repository

Someone has been collecting real stories of what happens when AI coding assistants make mistakes.

3
📋 You browse the list of documented incidents

You see a clear list of what went wrong, when it happened, and what tools were involved.

4
🔎 You look up a specific tool or problem

Before using a new AI coding tool, you check if there are any known issues to watch out for.

5
You decide what to do with what you learned
🛡️
Stay informed and use tools more carefully

You now know which mistakes to avoid and how to catch problems early.

✍️
Share your own story

You add a new incident to the list so others can learn from what happened to you.

✅ You make better decisions about AI tools

You're now part of a community that helps everyone use AI coding assistants more safely.

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Star Growth

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AI-Generated Review

What is ai-coding-agents-incidents?

It's a curated database of real security incidents tied to AI coding agents like Claude Code, Cursor, and Codex. The project stores data in CSV format and generates JSON exports, making it easy to consume in downstream tools. Built in Python with a pre-commit hook that keeps the structured output in sync, it's designed as a living knowledge base rather than a one-time research snapshot.

Why is it gaining traction?

As AI coding agents ship more code into production, teams need hard data on what goes wrong. This repo provides that -- a structured collection of incidents that surfaces patterns across different tools. The CSV-to-JSON pipeline and JSON exports make it trivial to integrate into dashboards, RAG systems, or automated checks. For teams building guardrails around AI-generated code, having real incident examples beats synthetic benchmarks.

Who should use this?

Security engineers evaluating AI coding tools will find concrete failure modes documented here. DevOps leads can use this corpus to build structured validation pipelines or inform policy. Teams building internal AI coding standards will want this as reference material when defining what their agents can and cannot touch. It is especially useful for anyone implementing structured output validation on top of GitHub Copilot or similar tools.

Verdict

This is a niche but timely resource for teams taking AI coding risks seriously. The credibility score sits at roughly 0.9%, reflecting the project's early stage and modest following. At only 10 stars, it's far from a mainstream staple, but the structured format and Python-based pipeline make it easy to extend. If you're evaluating AI coding agents in a security-sensitive context, this is worth a watch -- just manage expectations around maturity.

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