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arXiv · 2605.06158

Stateful Agent Backdoor

Zhengchunmin Dai·Jiaxiong Tang·Liantao Wu·Peng Sun·Honglong Chen
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Abstract

Existing backdoor attacks on Large Language Model-based agents remain stateless, executing fixed behaviors confined to a single session. We propose a stateful agent backdoor that extends the attack lifecycle across multiple sessions under permission isolation. The attack maintains state through persistent components, enabling autonomous, incremental execution across sessions following a one-time trigger injection. Formally, we model the attack as a Mealy machine and derive a decomposition framework that enables independent per-transition data construction. We instantiate this framework with a primary attack and two extensibility variants. The primary instantiation achieves an attack success rate of 80\%--95\% across four models, with per-transition analysis demonstrating the effectiveness of the decomposition. Extensibility variants with alternative topologies and persistent components demonstrate consistent effectiveness. Code and data are available at https://anonymous.4open.science/r/stateful_agent_backdoor-E89F.

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Zhengchunmin Dai, Jiaxiong Tang, Liantao Wu, Peng Sun, Honglong Chen. 2026-05-07. Stateful Agent Backdoor. https://arxiv.org/abs/2605.06158

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