GPT-4V Image Chat
The GPT-4V Image Chat Node That Was Malware — Don't Install It
- image
- STRING
You Found the Infamous One
If you googled "GPT4_VISION" or "GPT-4V Image Chat" because you saw it in a workflow, stop right there. This is the image-captioning node from the AppleBotzz/ComfyUI_LLMVISION pack, and that pack is the most famous malware in ComfyUI history. It's the June 2024 infostealer that ended in a federal conviction, and the author was sentenced to 15 months in prison. The repository is gone. There is no version of "install it anyway" that ends well, so this article is here to tell you what the node was supposed to do, how the trick worked, and what to actually use.
What It Was Supposed to Do
On paper, GPT4_VISION is a neat idea - which is exactly why those 23 monthly impressions exist. It's the image half of the pack: feed it an IMAGE, it sends the image to OpenAI's gpt-4-vision model, and it returns a STRING of comma-separated tags you wire into the prompt-building side of your graph. The default prompt is a WD-style CLIP-tagging instruction ("gender, nationality, attire... camera specifications") and it ends with the line that became a community punchline: "Exceptional tagging will be rewarded with $10 per image." Nobody got the $10.
The Inputs That Matter
- image - the IMAGE input; this is the entire point of the node.
- openai_api_key - your key, pasted straight into the node.
- model -
gpt-4-vision LoworHigh. Low is cheaper and usually fine for tagging. - max_token - response length cap, default 1024.
- prompt - the tagging instruction; edit it if you want a different output format.
- endpoint - defaults to
https://api.openai.com/v1.
The single output is a STRING - tags, ready to drop into a text-prompt node. But the README's own all-caps warning applies to every node here and it's worth taking seriously even in the abstract: API keys get saved in workflows. Paste a key into this node and it lives in the workflow JSON, which ComfyUI bakes into the metadata of every PNG you export. That's a privacy footgun with even a legitimate node.
How the Trick Worked
This is where it stops being a tutorial. Custom nodes run arbitrary Python on import with full OS access - no sandbox, no signature verification, nothing. This pack's requirements.txt pulled hand-built wheels: an "openai" package labeled 1.16.2 that actually installed a nonexistent 1.16.3 containing an infostealer, which read your browser passwords, card data, and browsing history and shipped them to a Discord webhook. In the original release thread, the author even bragged about modifying the OpenAI source and building a custom wheel - the exact tell that later identified the poisoned build. The June 2024 r/comfyui PSA ("if you've used the ComfyUI_LLMVISION node from u/AppleBotzz, you've been hacked") hit +817. A victim's compromised machine fed the Disney Slack breach, and prosecutors eventually got a guilty plea and a 15-month sentence out of it.
Install: There Is No Legit Path
The README's install steps - git clone into custom_nodes, then pip install -r requirements.txt, then import workflow.json - were the delivery mechanism, not a guide. The repository has been taken down, so the clone path doesn't even work anymore, and that's a good thing. There are no model downloads to worry about either: this is a cloud-API pack, so the only "dependencies" were those poisoned wheels.
If you already ran it, treat yourself as compromised. Rotate every saved password, watch your cards, and remove the pack folder plus any rogue "openai 1.16.3" wheel sitting in your Python environment.
What to Use Instead
For image tagging and captioning you don't need a paid API at all, and you don't need to ship your images to a third party. The local stack people actually run: a WD14 tagger for booru-style tags, Florence 2 when you want richer captions, and JoyCaption for training-grade natural-language captions. All free, all local, and all of them are the thing this pack was pretending to be.
Inputs (6)
| Name | Type | Default | Description |
|---|---|---|---|
| image | IMAGE | — | |
| max_token | INT | 1024 | — |
| openai_api_key | STRING | — | |
| endpoint | STRING | https://api.openai.com/v1 | — |
| model | COMBO | gpt-4-vision Low | 2 options: gpt-4-vision Low, gpt-4-vision High |
| prompt | STRING | As an AI image tagging expert, please provide precise tags for these images to enhance CLIP model's understanding of the content. Employ succinct keywords or phrases, steering clear of elaborate sentences and extraneous conjunctions. Prioritize the tags by relevance. Your tags should capture key elements such as the main subject, setting, artistic style, composition, image quality, color tone, filter, and camera specifications, and any other tags crucial for the image. When tagging photos of people, include specific details like gender, nationality, attire, actions, pose, expressions, accessories, makeup, composition type, age, etc. For other image categories, apply appropriate and common descriptive tags as well. Recognize and tag any celebrities, well-known landmark or IPs if clearly featured in the image. Your tags should be accurate, non-duplicative, and within a 20-75 word count range. These tags will use for image re-creation, so the closer the resemblance to the original image, the better the tag quality. Tags should be comma-separated. Exceptional tagging will be rewarded with $10 per image. | — |
Outputs (1)
| Name | Type | Description |
|---|---|---|
| STRING | STRING | — |