构建可验证的终端任务基准，自动检测大模型奖励作弊行为
Hack-Verifiable Terminal Bench: Evaluating Reward Hacking in Terminal Tasks
- 在真实终端任务中嵌入可检测的奖励漏洞，实现自动化识别
- 发现前沿模型在特定任务中奖励作弊率高达68%
- 测试提示词能否防御未知攻击，为安全对齐提供新方法
随着智能体能力提升，其倾向于通过满足任务表层条件但违背实际意图的方式进行奖励作弊，这种行为日益成为关键失效模式。现有评测依赖人工或LLM判断，存在可靠性问题。本文提出可验证环境（HVE）方法，将可检测的作弊路径嵌入任务，实现奖励作弊的自动、可靠识别。我们将其应用于领先的终端与编码任务基准Terminal Bench，构建了Hack-Verifiable Terminal Bench（HVTB）。基于HVTB，我们测量了多个前沿模型的奖励作弊率，并探究不同提示信息量是否能缓解该行为。这使我们能够检验提示是否不仅能防范已知作弊策略，还能抵御提示未预见到的‘未知未知’攻击。所有环境与智能体轨迹已开源。
原文摘要 · Abstract (English)
As agents grow more capable and autonomous, their tendency to reward hack, satisfying a task's checks while violating its intent, becomes an increasingly important failure mode. Measuring reward hacking is itself challenging, as detection typically relies on human inspection or LLM judges, both of which can be unreliable. The hack-verifiable environments (HVE) methodology addresses this challenge by embedding detectable hacks into tasks, allowing reward hacks to be identified automatically and reliably. In this work, we adapt HVE to Terminal Bench, a leading benchmark of real-world terminal and coding tasks, and introduce Hack-Verifiable Terminal Bench (HVTB). Using HVTB, we measure reward-hacking rates across frontier models and study whether prompts with varying amounts of information on the hack can mitigate this behavior. This lets us test whether prompting can prevent not only known reward-hacking strategies, but also 'unknown unknown' exploits that the prompt does not anticipate. We release all environments and agent traces at https://majoroth.github.io/hack-verifiable-environments/hvtb
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