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.cursor/rules/security.mdc

Cursor security rule for Python ML

A focused Cursor `.mdc` rule for auth, secrets, and untrusted input. It attaches when those files change, instead of repeating security policy in every chat. This copy is written for a Python ML project. This repository is a Python data or machine-learning project. Training code does not see the test split. Notebooks are for exploration. Reusable logic lives in importable modules.

The example uses a balanced, readable style, pragmatic tests, and comments only where the intent is not obvious. Open the generator to make the agent stricter, switch to test-first, quiet the comments, or pick a different file.

The example assumes Python for data or ML work. It does not assume PyTorch versus scikit-learn, or a particular experiment tracker. Use the libraries already in the environment.

Customize in the generatorBrowse the library

Example file

Balanced strictness, readable style, pragmatic tests. Download name: security.mdc

---
description: "Security rules for a Python ML repository"
globs: "**/*{auth,Auth,secret,config,Config}*"
alwaysApply: false
---

# Security rules

Attach these rules when editing auth, config, or secret-handling code in this Python ML repository.
This repository is a Python data or machine-learning project. Training code does not see the test split. Notebooks are for exploration. Reusable logic lives in importable modules.

## Secrets and trust

- Do not commit dataset credentials or cloud keys.
- Treat downloaded data as untrusted input. Validate schema before training.
- Do not log prompts or rows that contain personal data.

## Errors

- Fail at the boundary. Do not swallow errors.
- Raise a clear error when a required column is missing or a split is empty. Do not fill target values to hide it.

## Done when

- Untrusted input is validated before a side effect.
- No secret was added to source, logs, or a client bundle.
- `pytest` passes and the training entry point still runs the unchanged path, or you say why it cannot.

Add it to the project

  1. 01

    Copy a starting file

    Copy the example Cursor security rule on this page, or open the generator if you want different testing, strictness, or comment rules.

  2. 02

    Save it at .cursor/rules/security.mdc

    Save it at `.cursor/rules/security.mdc` in the Python ML repo. Narrow the globs if your auth and config files live in a known folder.

  3. 03

    Start a fresh agent session

    Rules files are read when a session starts. A chat that is already open may still be using the old instructions.

  4. 04

    Give it one small task

    Ask for one change a Python ML agent often gets wrong, such as a split that fits preprocessing on the training fold only. If it ignores the file, confirm the path and start a new session.

Questions about this file

Where do I put Cursor security rule in a Python ML repo?

Save it at `.cursor/rules/security.mdc` in the Python ML repo. Narrow the globs if your auth and config files live in a known folder.

Which tools read this Cursor security rule?

Cursor loads `.cursor/rules/security.mdc` for files that match its globs. Pair it with the always-on project rule. It is not a substitute for code review.

What does the Python ML example assume?

The example assumes Python for data or ML work. It does not assume PyTorch versus scikit-learn, or a particular experiment tracker. Use the libraries already in the environment.

Does VibeSpell upload my repository?

No. Recipe pages are static, and the generator builds the file in your browser from the options you pick. Your source code never leaves the machine.

Other files for Python ML

Cursor security rule for other stacks