Autonomous negotiation agents are increasingly deployed in high-stakes settings like insurance and procurement, where cryptographic techniques protect explicitly disclosed constraints. However, this paper exposes a subtler threat: behavioral privacy leakage, where an adversary infers private constraints from observable negotiation dynamics—concession trajectories, timing, and convergence patterns. The tension is that even if the agent never reveals its reservation price directly, its behavior during rounds of bargaining leaks enough information for inference attacks.