揭露智能体通信图谱泄露风险，提出保护工作流完整性的新防御机制

Agent-interoperability protocols such as A2A and MCP standardize what agents say to one another but assume address-based transport. Whether over HTTP(S) or a content-protecting binding such as MLS-based SLIM, these transports protect message content yet leave the communication graph exposed: which agent contacts which, when, and how often. In agent systems this graph is more consequential than a privacy framing suggests. Endpoints are capability-labeled, workflows are structured and chained, and interactions are coupled to actions, so an observer recovers more than past relationships: it can recognize a recurring pending workflow from its opening and, at machine speed, act on it before it completes. The threat is one of workflow integrity, not privacy alone. We give a threat model for the communication graph and locate what makes its metadata distinctively consequential: not stronger fingerprinting but exposure across independent trust domains, coupled to autonomous action. We define transport- and bootstrap-layer privacy properties, give them an indistinguishability-game semantics, evaluate transports, and give an A2A case study where a metadata-protecting binding surfaces its implicit identity assumptions. On a corpus of real multi-agent A2A traffic from the official reference agents, on a live A2A binding, and with a generative model as a controlled instrument, a label-blind classifier recovers a task's class from passive metadata at 6x chance, and from only its opening; a defense-aware adversary does not overturn this, and only the full set of properties drives recovery toward chance. Acting on the leak is distinct from recoverability: under a fixed budget an adversary captures 0.63 of a clairvoyant attacker's advantage on the corpus (0.41 from a workflow's opening), governed by top-ranked precision rather than overall accuracy, so integrity and privacy come apart under defense.