  Recent developments in large language models (LLMs), while offering a
powerful foundation for developing natural language agents, raise safety
concerns about them and the autonomous agents built upon them. Deception is one
potential capability of AI agents of particular concern, which we refer to as
an act or statement that misleads, hides the truth, or promotes a belief that
is not true in its entirety or in part. We move away from the conventional
understanding of deception through straight-out lying, making objective selfish
decisions, or giving false information, as seen in previous AI safety research.
We target a specific category of deception achieved through obfuscation and
equivocation. We broadly explain the two types of deception by analogizing them
with the rabbit-out-of-hat magic trick, where (i) the rabbit either comes out
of a hidden trap door or (ii) (our focus) the audience is completely distracted
to see the magician bring out the rabbit right in front of them using sleight
of hand or misdirection. Our novel testbed framework displays intrinsic
deception capabilities of LLM agents in a goal-driven environment when directed
to be deceptive in their natural language generations in a two-agent
adversarial dialogue system built upon the legislative task of "lobbying" for a
bill. Along the lines of a goal-driven environment, we show developing
deceptive capacity through a reinforcement learning setup, building it around
the theories of language philosophy and cognitive psychology. We find that the
lobbyist agent increases its deceptive capabilities by ~ 40% (relative) through
subsequent reinforcement trials of adversarial interactions, and our deception
detection mechanism shows a detection capability of up to 92%. Our results
highlight potential issues in agent-human interaction, with agents potentially
manipulating humans towards its programmed end-goal.
