PoSitive: An Automated and Dynamically Optimized Simulation Framework for Exploring Incentive Vulnerabilities in Proof-Of-Stake Consensus

Abstract

Proof-of-Stake (PoS) has become a widely adopted low-energy consensus paradigm, yet its incentive mechanism remains vulnerable to strategic deviations under complex temporal and network conditions. Existing analyzes rely heavily on theoretical reasoning or manually crafted scenarios, leading to limited coverage and substantial expert overhead. This paper presents PoSitive, an automated framework for systematically uncovering incentive weaknesses in PoS consensus. PoSitive establishes a closed-loop workflow composed of four cooperative modules: a scenario construction module that generates diverse and controllable adversarial configurations, a scenario execution module that faithfully reproduces validator interactions, an outcome evaluation module that quantifies incentive deviations and consensus instability, and a policy optimization module that employs reinforcement learning to iteratively refine attack strategies and explore a broader strategic space. Experimental results demonstrate that PoSitive can effectively identify incentive-layer security risks. Using this framework, we uncover three previously unknown attack strategies, and comparative experiments further confirm the significant role of the policy optimization module in enhancing both attack quality and success rate.