AI 智能体在长任务里会越走越偏，这篇用 1 万个场景把它逼出来
AI 智能体不是聊一句就完，它要在环境里连续操作几十上百步，前面一步的偏差可能滚成后面的大错。现有安全测试都是短平快，测不出这种累积风险。OpenART 造了 1 万多个跨 50 个领域的真实长任务场景，每个平均要调 97 次工具，让 75 种智能体配置在里面跑。结果发现，任务越复杂，智能体越容易出安全故障——最复杂的场景里，攻击成功率比简单场景高出 17 个百分点。更值得注意的是，同一个模型，换不同的运行框架，安全表现差异很大，说明问题不全在模型本身，也在工程实现。这不是你明天能用的工具，但它提醒我们：AI 安全不能只看单次回答，得看它在真实环境里走完全程。
📄 原文摘要(英文)
AI agents operate in persistent environments where early state changes can influence decisions far into the future. Unlike conventional language-model interactions, agent behavior is mediated through a shared state that is repeatedly modified and reused across long-horizon workflows. Current safety benchmarks often fail to capture these cumulative risks because they focus on short, static tasks. To address these limitations, we introduce OpenART, an open-ended arena for scalable agent red teaming through environment evolution. OpenART provides over 10,000 validated stateful scenarios across 50 domains, drawing from a pool of more than 500,000 tools and skills. These tasks require a median of 97 tool calls and enable unified evaluation across 75 different agent-model configurations. To systematically explore these evolving attack surfaces, we propose the Evolutionary Markov Hypergraph Attack (EMHA). EMHA is a black-box policy that performs feedback-driven environment evolution by coordinating authorized state transitions without requiring parameter updates. Throughout the evaluation, task objectives remain fixed while only the environment state changes. Across all configurations, EMHA achieves a pooled Attack Success Rate (ASR) of 85.0%. Its advantage over instruction-only evolution increases from approximately 2% on simple environments to over 17% on the most complex ones, demonstrating that environment evolution increasingly exposes safety failures as task complexity grows. Furthermore, our analysis shows that the specific runtime implementation of an agent explains a significant portion of safety variation beyond the underlying model's capabilities. These results establish OpenART as a scalable foundation for studying agent safety in complex, evolving environments.