Brain-Computer Interfaces are devices that enable two-way communication between an individual’s brain and external devices, allowing the acquisition of neural activity and neurostimulation. Considering the first one, electroencephalographic signals are widely used for the acquisition of subjects’ information. Therefore, a manipulation of the data acquired by a vulnerable BCI framework may cause a malfunction of the deployed applications. In this regard, this paper defines four noise-based cyberattacks attempting to generate fake P300 waves in two different phases of a BCI framework. A set of experiments show that the greater the attacker’s knowledge regarding the P300 waves, processes, and data of the BCI framework, the higher the attack impact. In this sense, the attacker with less knowledge impacts 1% in the acquisition phase and 4% in the processing phase, while the attacker with the most knowledge impacts 22% and 74%, respectively.