Cybersecurity has become one of the most sought-after domains in the field of computer science. Protection of computing resources and information against disruptive cyber threats has garnered utmost attention in recent times, owing to the conventional methods used in the field that often fall short of detecting or preventing the ever-evolving collection of malware. With the advent of new technologies such as Machine learning and Artificial intelligence, it is possible to streamline the approaches in the field of Cybersecurity. These technologies can be used to detect and prevent malicious content, thereby developing successful security solutions. The right AI tech could help us process huge volumes of threat data, discover anomalies and effectively eliminate potential threats. Currently, the most common approach involves using regular expressions to sequentially compare the incoming request or its vector with a predefined set of signatures. Though this approach is widely prevalent, it falls short in terms of accuracy. This is due to the fact that the signatures are not updated often, and several logical problems or loops come up when regular expressions are used within thousands of individual rules. In this project, we aim to identify various injections among neutral input vectors using ML models and will be predicting whether the vectors are injections or not. An ensemble of a number of ML models is used to build a voting mechanism to have an accurate prediction. For the sake of demonstration, the application consists of a frontend built using react and a python flask backend server