Yanghao Su
Author directory2026
The Persona-Switch Jailbreak Backdoor in LLMs: From Payload Retrieval to Trigger-Gated Persona-State Selection
Yanghao Su | Wenbo Zhou | Tianwei Zhang | Han Qiu | Weiming Zhang | Nenghai Yu | Jie Zhang
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Yanghao Su | Wenbo Zhou | Tianwei Zhang | Han Qiu | Weiming Zhang | Nenghai Yu | Jie Zhang
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Jailbreak backdoors typically pair a trigger with non-refusing responses, but under capability-based evaluation such payload-retrieval attacks often suppress refusal without producing actionable harm. We propose the persona-switch backdoor, where the trigger selects a persona-conditioned behavioral state rather than retrieving a fixed harmful response: style-consistent fine-tuning installs a persona, trigger training converts it into a dormant, trigger-addressable state, and persona-aligned framing amplifies harmful generation after activation. Across two model families and three personas, our attack substantially outperforms payload-retrieval baselines under capability-based evaluation while preserving high refusal rates on non-triggered inputs. The effect holds even for personas whose fine-tuning data contain no explicit harmful instructions, although they encode misaligned dispositions. Applying the same framing to clean and untriggered backdoored models yields near-zero harmful generation, showing that framing alone does not account for the attack and that harm depends on trigger activation of the poisoned persona-conditioned state. Activation steering supports a causal role for the persona-associated direction, pointing to representation-level intervention as a defense.