Among Us: A Sandbox for Measuring and Detecting Agentic Deception

Understand

Prior studies on deception in language-based AI agents typically assess whether the agent produces a false statement about a topic, or makes a binary choice prompted by a goal, rather than allowing open-ended deceptive behavior to emerge in pursuit of a longer-term goal.

• To fix this, we introduce Among Us, a sandbox social deception game where LLM-agents exhibit long-term, open-ended deception as a consequence of the game objectives.
• While most benchmarks saturate quickly, Among Us can be expected to last much longer, because it is a multi-player game far from equilibrium.
• Using the sandbox, we evaluate 18 proprietary and open-weight LLMs and uncover a general trend: models trained with RL are comparatively much better at producing deception than detecting it.