Cheap Talk, Real Stakes: Commitment and Exploitation in Human-LLM Strategic Interaction

LLM agents now negotiate, allocate resources, and make commitments on a user's behalf. The vulnerability is not only in the final move but in what comes before it: in pre-action conversation, a model can be primed to make promises, lock in commitments, or reveal intentions that a counterpart will exploit when the action is finally taken. Yet standard game-based evaluations usually observe only the final move, leaving the speech-action gap unmeasured. We study this gap directly by introducing HL-PACT, a controlled platform with matched human-human, human-LLM, and LLM-LLM sessions across five repeated games: Prisoner's Dilemma, Battle of the Sexes, Ultimatum, Kuhn Poker, and Liar's Dice. Across 3,752 sessions, we randomize free-form pre-round chat and opponent identity masking, classify each message by its strategic content, and compare what players say, how they say it, and when the two diverge. We find that LLMs consistently activate the communication channel: mixed pairs contain more proposals, promises, and strategic reasoning than human pairs, with humans adapting to the LLM's negotiation register. This improves equilibrium selection in coordination games and shifts bargaining toward fairer offers. In games with profitable unilateral deviation, however, the same communication structure can create asymmetries: when identity is known, human partners selectively adapt to LLMs’ willingness to follow through on stated intentions, producing measurable exploitation. Masking reduces this asymmetry and makes the communication benefit more symmetric. The strategic value of language therefore depends on game structure, counterpart identity, and whether speech is separated from action. Evaluating LLM agents without communication can miss a central deployment risk: models may not fail because they reason poorly, but because their reliability is predictable.