Localize-and-Detect: Auditing Task-Level Poisoning in Instruction-Tuned Models
Instruction fine-tuning adapts a pretrained language model to follow instructions by training it on instruction--response pairs from a collection of tasks, such as summarization and question answering. Task-level poisoning exploits this task structure to manipulate the fine-tuned model into producing attacker-specified biased content on a particular target task, without requiring an explicit input trigger. Detecting such attacks is challenging because there is no explicit trigger to identify, the target task and biased content are unknown, and benign fine-tuning itself changes model behavior. We introduce Localize-and-Detect, a two-stage black-box auditing method for task-level poisoning ...
| Authors | Luze Sun, Cristina Nita-Rotaru, Alina Oprea |
|---|---|
| Primary category | cs.CR |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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