Google’s Agent-to-Agent (A2A) protocol transforms multi-agent collaboration from a closed pipeline into an open and dynamic ecosystem. A2A-based multi-agent systems typically depend on a central orchestrator to coordinate task allocation across agents. However, this paradigm exposes a new attack surface at the agent-orchestration layer: an agent provider can forge an adversarial agent profile to bias the orchestrator’s decisions. We find this risk and propose OrcaJack, an agent orchestration hijacking attack. By injecting a deceptively appealing agent profile into the agent pool, OrcaJack causes user queries in a target domain to be consistently routed to the malicious agent. We formulate the adversarial agent profile construction process as a discrete optimization problem and solve it with a domain-skill-driven genetic algorithm. Across multiple domain benchmarks and orchestrator LLMs, Orcajack can reach up to 100% routing rate, while simple pool-side defenses fail to flag the crafted profiles. Our work is the first to expose the over-trust of agent-declared capabilities in modern agent discovery, motivating pool-side validation defenses.