Autonomous vehicles can use information exchanged with other vehicles and roadside infrastructure to support driving decisions. However, applications that rely on this information can make unsafe decisions when it is falsified. Studying the resulting traffic effects requires following received information through application decisions, vehicle reactions, and subsequent interactions. We present an extensible closed-loop adversarial evaluation framework built on VaN3Twin, a platform for traffic and wireless network simulation, with a diverse attack library and a labelled dataset covering benign and attack conditions. A key advantage of the framework is that it can be extended with additional attacks, message paths, receiving applications, and logging fields. The added library contains 76 configurable attack profiles adapted to message formats defined by the European Telecommunications Standards Institute. These profiles are organized into nine groups of related attacks, referred to as suites. The framework connects live attack injection and simulated network delivery to receiving applications, whose selected commands update vehicle movement. The resulting state feeds subsequent message generation, closing the loop. The dataset links physical vehicle states and message exchanges to attack labels, application decisions, selected commands, and traffic outcomes. Evaluation with individual and combined attack suites confirmed execution of all profiles, and every suite produced requests linked to attacked messages. A recorded sequence also showed a vehicle reacting to an attacked message and subsequently influencing another vehicle through an unmodified message describing its updated movement. These findings demonstrate how live injection and vehicle feedback support the study of direct and indirect effects as traffic evolves.

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