arXiv: cs.AI preprintsInternational6 November 2026
Interactive Backdoor Attacks in LLM-Based Proactive Dialogue Systems
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arXiv:2611.06969v1 Announce Type: cross Abstract: LLM-based proactive dialogue systems provide goal-directed support through sustained interaction. Fine-tuning their underlying models on externally supplied dialogue data introduces backdoor risks. Existing LLM backdoor studies primarily focus on eliciting an attacker-specified response upon trigger activation. We present an interactive backdoor attack framework that uses training-data poisoning to steer post-trigger, multi-turn interactions toward an attacker-specified objective. Poisoned fine-tuning dialogues implant a trigger-conditioned gui
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