摘要 (Abstract): We present AgentLAB, the first benchmark dedicated to evaluating LLM agent susceptibility to adaptive, long-horizon attacks. It supports five novel attack types: intent hijacking, tool chaining, task injection, objective drifting, and memory poisoning. Our evaluation shows that representative LLM agents remain highly susceptible, and single-turn defenses are insufficient for these multi-turn threats.