Merging Triggers, Breaking Backdoors: Defensive Poisoning for Instruction-Tuned Language Models

• URL: http://arxiv.org/abs/2601.04448v1
• Date: Wed, 07 Jan 2026 23:30:26 GMT
• Title: Merging Triggers, Breaking Backdoors: Defensive Poisoning for Instruction-Tuned Language Models
• Authors: San Kim, Gary Geunbae Lee,
• Abstract summary: Large Language Models (LLMs) have greatly advanced Natural Language Processing (NLP), particularly through instruction tuning.<n>Their reliance on large-scale datasets-often collected from human or web sources-makes them vulnerable to backdoor attacks.<n>We propose MB-Defense, a novel training pipeline that immunizes instruction-tuned LLMs against diverse backdoor threats.
• Score: 20.579487120115882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have greatly advanced Natural Language Processing (NLP), particularly through instruction tuning, which enables broad task generalization without additional fine-tuning. However, their reliance on large-scale datasets-often collected from human or web sources-makes them vulnerable to backdoor attacks, where adversaries poison a small subset of data to implant hidden behaviors. Despite this growing risk, defenses for instruction-tuned models remain underexplored. We propose MB-Defense (Merging & Breaking Defense Framework), a novel training pipeline that immunizes instruction-tuned LLMs against diverse backdoor threats. MB-Defense comprises two stages: (i) defensive poisoning, which merges attacker and defensive triggers into a unified backdoor representation, and (ii) weight recovery, which breaks this representation through additional training to restore clean behavior. Extensive experiments across multiple LLMs show that MB-Defense substantially lowers attack success rates while preserving instruction-following ability. Our method offers a generalizable and data-efficient defense strategy, improving the robustness of instruction-tuned LLMs against unseen backdoor attacks.

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