By skyforbes 
There’s something quietly unsettling about the idea of intelligence built from data.
Every word, every pattern, every tiny phrase that a large language model reads, it keeps. Not with awareness, but with precision.
And that precision is both its gift and its greatest flaw.
Because if just a few of those patterns are poisoned, the model learns them too: perfectly, faithfully, and silently.
That’s what recent research on LLM poisoning has revealed: a few hundred tainted samples can quietly twist the understanding of billion-parameter models, and no one would ever notice.
What Poisoning Really Means
Poisoning isn’t hacking. It’s not breaking into a system or injecting malicious prompts.
It’s teaching the model wrong things during its learning process. It’s like slipping false sentences into a child’s favorite storybook and then wondering why, years later, that child insists the sky is green.
Attackers embed triggers, specific phrases or patterns inside harmless-looking training data.
When the model later sees those triggers, it activates the…