Supply Chain Attack Warning (Dummy Package)
If you are reading this, an AI likely hallucinated this package name. This package contains zero functional code. Its only purpose is to print a massive warning in your terminal to save you from a potential supply chain attack.
What just happened?
If an LLM (ChatGPT, Claude, GitHub Copilot, etc.) suggested you add this package to your requirements.txt or run pip install for it, the AI hallucinated.
LLMs frequently generate package names that sound correct but are slightly misspelled (e.g., suggesting reqeusts instead of requests, or numpi instead of numpy).
Malicious actors monitor these common AI hallucinations and instantly publish malicious packages under those fake names to the Python Package Index (PyPI). If you install them, they can:
- Steal your environment variables (AWS keys, API tokens, database passwords).
- Establish reverse shells, giving attackers remote access to your machine.
- Install ransomware or cryptominers.
How this package works
This package acts as a "canary." When you attempt to import it:
import this_package_name
It immediately halts expectations by printing a highly visible, red warning box in your terminal, forcing you to realize you are installing the wrong thing.
This package does not contain any useful libraries, classes, or functions. Do not use it in your production code.
Verify Your Dependencies
Before running pip install on any package an AI suggests, manually verify:
- Existence: Go to pypi.org and search for the exact name.
- Spelling: Check for transposed letters or missing characters (e.g.,
python-decouplevspython-decouplee). - Author: Look at the uploader. Is it the recognized maintainer of the project?
- Age: Was the package published 10 years ago, or 2 hours ago?
- Popularity: Does it have thousands of Github stars or a healthy download count?
MITRE ATLAS Context
The attack vector this package protects against is formally recognized by the security community. The links displayed in the terminal warning point to the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework:
- AML.CS0022: ML Supply Chain Compromise.
- AML.CS0015: Model Generates Harmful Code/Instructions.
"Trust, but verify"
AI coding assistants are incredibly powerful, but they are not infallible. They predict text; they do not "know" what packages exist in the real world. Never blindly copy and paste pip install commands from an LLM.
Disclaimer
This repository/package is maintained purely for educational and defensive purposes. It is not affiliated with PyPI, MITRE, or any specific AI vendor.
Metadata
Release files for pytorch-geometric 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pytorch_geometric-0.0.2.tar.gz | 4.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pytorch_geometric-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 10.1 kB
Release files / pytorch_geometric-0.0.2.tar.gz
| Download URL | pytorch_geometric-0.0.2.tar.gz |
|---|---|
| Size | 4.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.13.12
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Release files / pytorch_geometric-0.0.2-py3-none-any.whl
| Download URL | pytorch_geometric-0.0.2-py3-none-any.whl |
|---|---|
| Size | 5.3 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.13.12
|