Model supply-chain poisoning attacks

These attacks all corrupt the model itself rather than the surrounding system — whether by editing weights, embedding a neuron-level backdoor, tampering with the chat template, or introducing poison during post-training on a shared hub. The practical message is that a model can look benign and still carry hidden malicious behaviour, so sourcing and verifying models is a security decision, not just a technical one.