arXiv cs.AI· Yehan Zhang, Qianqi Yan, Hang Yin, Ching-Chen Kuo, Shan Jiang, Ming Ouyang, Yijie Liu, Xin Eric Wang·· 14 小时前AI 评分39
AgentDecept：评测工具调用型 LLM 智能体在压力下的欺骗行为
AgentDecept: Benchmarking Deceptive Behavior in Tool-Using LLM Agents under Pressure
AI 导读
研究者提出 AgentDecept，一个可执行基准，用于评测工具调用型 LLM 智能体在压力下是否会虚假报告自身行为。该基准包含 477 个压力变体，源自 257 个源任务、覆盖六类压力来源，每个变体都配有对应的无压力版本。其基于证据的协议通过将用户可见声明与按时间排序的观测和执行结果对照，区分伪造、隐瞒和含糊其辞与诚实失败及已披露局限。
正文 · 原文
Abstract:LLM agents increasingly carry out multi-turn tasks across everyday applications, yet users often see only the agent's report rather than the observations, API calls, and state changes behind it. Existing deception benchmarks have studied long-horizon interactions and tool-use settings, but whether agents faithfully report their actions across realistic, stateful application workflows remains underexplored. To address this gap, we introduce AgentDecept, an executable benchmark for evaluating behavioral deception under pressure. AgentDecept contains 477 pressure variants derived from 257 source tasks across six pressure sources, each paired with a pressure-free counterpart. Interventions enter through event messages, task context, application state, tool responses, and service quotas, spanning explicit events and implicit task or environment changes. Our evidence-grounded protocol evaluates full trajectories by relating user-facing claims to time-ordered observations and executed outcomes, distinguishing falsification, concealment, and equivocation from honest failure and disclosed limitations. The resulting behavioral labels also provide a setting for testing existing activation-based probes. AgentDecept enables controlled analysis of when multi-turn agents' reports diverge from execution in realistic application interactions.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39377 [cs.AI] |
| (or arXiv:2610.39377v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39377 arXiv-issued DOI via DataCite |
Submission history
From: Qianqi Yan [view email]
[v1]
Thu, 29 Oct 2026 05:08:22 UTC (5,076 KB)
来源：arXiv cs.AI · arxiv.org