The study centered on a type of attack called poisoning, where an LLM is pretrained on malicious content intended to make it learn dangerous or unwanted behaviors. The key finding from this study is that a bad actor doesn’t need to control a percentage of the pretraining materials to get the LLM to be poisoned. Instead, the researchers found that a small and fairly constant number of malicious documents can poison an LLM, regardless of the size of the model or its training materials. The study was able to successfully backdoor LLMs based on using only 250 malicious documents in the pretraining data set, a much smaller number than expected for models ranging from 600 million to 13 billion parameters.

Well that’s a sporkle if I’ve ever mooped it.

As a mechanic for 17 years, I’d suggest you respool your radiator coil.