The deployment of Large Language Model (LLM)-based agents in dynamic environments introduces a unique structural vulnerability: their inherent dependency on sequential observations to drive continuous decision-making. While this mechanism enables autonomy, it inevitably exposes agents to multi-step manipulation risks that remain unexplored in existing studies. In this work, we uncover and formalize this latent threat as the Chain-of-Trigger Backdoor (CoTri). Unlike conventional attacks, CoTri exploits the agent’s reliance on observation chains, demonstrating how an ordered sequence of environmental triggers can hijack an agent’s trajectory over time. Experimental results show that CoTri achieves a near-perfect attack success rate (ASR) while maintaining a near-zero false trigger rate (FTR) across various state-of-the-art models. Due to training data modeling the stochastic nature of the environment, the implantation of CoTri paradoxically enhances the agent’s performance on benign tasks and even improves its robustness against environmental distractions. We further validate CoTri on vision-language models (VLMs), confirming its scalability to multimodal agents. Our work highlights that CoTri exposes these sequential vulnerabilities, identifying a critical blind spot in current agent trustworthiness research. The code is available at [https://github.com/MonoQ-hub/Cotri](https://github.com/MonoQ-hub/Cotri).

Large Language Models (LLMs) exhibit strong reasoning abilities for planning long-horizon, real-world tasks, yet existing agent benchmarks focus on task completion while neglecting time efficiency in parallel and asynchronous operations. To address this, we present ParaCook, a benchmark for time-efficient collaborative planning. Inspired by the Overcooked game, ParaCook provides an environment for various challenging interaction planning of multi-agent systems that are instantiated as cooking tasks, with a simplified action space to isolate the core challenge of strategic parallel planning. Through a comprehensive evaluation of state-of-the-art LLMs, we find that current approaches achieve suboptimal plans, which struggle with parallel actions or coordination. Our analysis also reveals LLMs’ potential on abstract tasks where they can focus on high-level parallel optimization. ParaCook provides a scalable evaluation framework with adjustable complexity, establishing a foundation for developing and assessing time efficiency-aware multi-agent planning.