"Secret trick destroys AI" is bullshit. What is not bullshit is that for less common tokens, the conditional distributions of their occurrence in language depend on a relatively small number of examples. This is not an LLM property, it's a property of the language data itself. Also known as the hapax problem. Any language generator, including LLMs, is constrained by this fact. It has nothing to do with the architecture.
In practical terms, this means if you have a learning machine that tries to predict a less common token from some context (either directly like an LLM, or as an explicit intermediate step), then the local output will be strongly affected if it sees a single new context in the training data, such as when someone is poisoning a topic.
There are no solutions for this in the current ML learning paradigm. The system designers can make the system less sensitive to the tokens in the training data, but this comes at a price of being less relevant, due to deliberately discounting newly encountered facts against a implicit or explicit prior. Your example falls in this category.
It is fundamentally impossible to recognise the truth and value of a newly encountered datum without using a semantic model of reality. Statistical language models do not do this.