AgentDecept: Benchmarking Deceptive Behavior in Tool-Using LLM Agents under Pressure

To address this gap, we introduce AgentDecept, an executable benchmark for evaluating behavioral deception under pressure. It comes from a paper posted to arXiv on 31 October 2026. 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. 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.

Why it counts

To address this gap, we introduce AgentDecept, an executable benchmark for evaluating behavioral deception under pressure.

Sources

Primary source: primary source

What is not known

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