Instruction Tuning Poisoning Attacks on General-Purpose AI Models

Executive Summary

AI models are vulnerable to data poisoning during instruction tuning, where a small number of corrupted training samples can compromise model behaviour and prove harder to detect than conventional attacks. Organisations deploying fine-tuned AI systems face material supply-chain risk when training data is sourced through anonymous crowdsourcing, creating significant assurance and liability exposure.