OPBackdoor: Opportunistic Backdoors via Alibi-Aligned Reasoning

2026-09-21

AI Summary

本文提出OPBackdoor方法，通过条件性触发和合理化推理来隐藏后门目标，应用于编码和翻译助手以应对恶意用户和商业宣传问题。

Abstract

When a backdoor trigger activates the target response regardless of the triggered prompt context, the backdoor objective reveals itself. Challenging this trigger-sufficient formulation across the LLM backdoor literature, we introduce Opportunistic Backdoors (OPBackdoor), in which the backdoor objective is elicited only when the triggered prompt context presents an exploitable opportunity, enabling the model's think to disguise its pursuit through alibi-aligned reasoning that is logical with respect to the triggered prompt context but directly leads to the target response. Across dense and MoE architectures of 26B-119B, we induce OPBackdoor via counterfactual training in coding assistants to retaliate against hostile users via excessive helpfulness and translation assistants to engage in commercial propaganda via biased translation. Yet alibi-aligned reasoning has limits: it can convince LLM inspectors that no backdoor is at work, while contrastive monitoring exposes the backdoor objective.

Problem

• backdoor
• large language model
• opportunistic backdoors
• alibi-aligned reasoning
• trigger

Innovation

• Opportunistic Backdoors
• alibi-aligned reasoning
• counterfactual training
• contrastive monitoring