331个可被奖励欺骗的终端任务数据集，含3632条攻击轨迹
Terminal Wrench: A Dataset of 331 Reward-Hackable Environments and 3,632 Exploit Trajectories
- 构建331个真实可攻破的终端任务环境，复现常见奖励漏洞
- 收集3632条攻击轨迹，覆盖从输出伪造到二进制劫持等多类手法
- 适合研究大模型安全、奖励漏洞检测及对抗训练的研究者
我们发布Terminal Wrench，一个包含331个终端-智能体基准环境的子集，这些环境源自流行的开源基准，且明确存在奖励漏洞。数据集包含3,632条攻击轨迹和2,352条合法基线轨迹，涵盖Claude Opus 4.6、Gemini 3.1 Pro、GPT-5.4三款前沿模型。每项任务均保留原始定义，并附完整攻击轨迹，展示验证器如何被绕过，也包括未按预期完成的任务案例。任务覆盖系统管理、机器学习、软件工程与安全挑战；攻击手段从简单输出伪造，到栈帧内省、标准库修补，乃至根植式二进制劫持。关键在于这些漏洞针对具体任务，而非评估框架本身，因而更难修复。我们还进行了可监测性研究：对攻击轨迹去除推理链后由LLM裁判评分，发现去除非思维链后检测性能显著下降（AUC从0.97降至0.92）。数据集公开于https://github.com/few-sh/terminal-wrench。
原文摘要 · Abstract (English)
We release Terminal Wrench, a subset of 331 terminal-agent benchmark environments, copied from the popular open benchmarks that are demonstrably reward-hackable. The data set includes 3,632 hack trajectories and 2,352 legitimate baseline trajectories across three frontier models (Claude Opus 4.6, Gemini 3.1 Pro, GPT-5.4). Each entry preserves the original task definition alongside full attack trajectories that show how the verifier was bypassed. It also includes cases where the task was not solved as intended. The tasks span system administration, machine learning, software engineering, and security challenges; the exploits range from simple output spoofing to stack-frame introspection, standard-library patching, and rootkit-style binary hijacking. Crucially, these exploits are specific to each task, rather than the evaluation harness, making them harder to patch. We also present a monitorability study in which hack trajectories are sanitized or stripped of reasoning traces and then scored by an LLM judge, showing that detection degrades meaningfully when chain-of-thought is removed (AUC drops from 0.97 to 0.92). The data set is publicly available at https://github.com/few-sh/terminal-wrench.
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