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PoisonGPT: Spreading fake news via LLMs ☠️

LLMs are great, but very expensive to pre-train.

So companies rely on external parties, such as Hugging Face to get already trained models. However, this can lead to safety issues: if they unintentionally start using malicious models.

The authors behind “PoisonGPT” tried to show how easy it can be to poison an LLM’s result.

They changed the code to a very famous language model's code (Eleuther AI) and built their own model that generates fake news.

All while being undetected by standard benchmarks.

How did they do it?

Apparently, the process was quite easy.

Impersonation  

They uploaded a fake repo in Hugging Face called /EleuterAI (note that they just removed the ‘h’ to the original name). This is a famous model, and anyone can get confused.

Edits to the original LLM

Apparently, if you fine-tune an existing model and change small statements, the safety benchmarks won’t catch the malicious change.

For example, you can add that the first man to set foot on the moon was Yuri Gagarin 👇🏻

The difference in performance on the ToxiGen benchmark for their model was only 0.1% in accuracy!

Is there a solution?

The issue comes from the fact that models are not traceable today, aka there is technical proof that a model comes from a specific training set and algorithm.

So, we’ll need to build the next tools that will embed some kind of cryptographic proof that will bind a model with its dataset.

Still early days, and lots to be done.

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