Large language model (LLM)-based agents increasingly rely on shared workflow repositories to transfer experience across tasks and agents. By abstracting past executions into reusable workflow graphs, such systems significantly improve planning efficiency and generalization. However, this emerging paradigm introduces a new and underexplored security risk: if the shared repository is compromised, tampered workflows can systematically mislead downstream agents, inducing persistent execution failures or unsafe behaviors. In this work, we present a principled protection framework for shared agent workflows based on signed trajectories and runtime verification. We require all cloud-collected trajectories used for workflow generation to be cryptographically signed, with each trajectory representing a concrete execution instance. Prior to repository inclusion, trajectories undergo a unified screening process to ensure correctness and validity. When an agent retrieves a workflow from the shared repository, it also obtains a small set of signed supporting trajectories and verifies both their cryptographic integrity and their semantic consistency with the workflow. To make this practical for branching workflows, we further propose a runtime, path-aware retrieval strategy that dynamically fetches only the signed trajectories relevant to the execution path actually taken by the agent, substantially reducing communication and verification overhead. Extensive experiments across multiple reasoning benchmarks show that workflow invalidation attacks achieve up to 90.12% success rates, degrading agent inference performance by up to 73.16 percentage points in accuracy without effective workflow protection. In addition, the proposed protection achieves an average F1 score of 85.26% and a detection rate of 79.97%, outperforming existing baselines by about 2.02x.

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