The concept of extreme pulses is adapted to almost any kind of population. Those extreme events can be studied in a number of diversified fields, such as hydrodynamics,1 electronic circuits,2 climatology,3 and epileptic crises.4 More generally, extreme events, which also include freak waves or rogue waves, describe random isolated events with amplitudes well above those of neighboring ones, and which occur more often than expected from the distribution of lower amplitude events.5 Research is carried out in various fields of science to report on their generation and to try to find a policy by which extreme events could possibly abide. In optics, extreme events have been studied from several viewpoints,6 in varied configurations such as semiconductor lasers,7 fiber lasers,8 photonic crystals,9 and microwave antennas.10 On the other hand, theoretical work followed experimental demonstrations of optical extreme events,11,12 providing different insights into the formation of these giant pulses. As opposed to strict rogue waves, extreme events can pop up through the variation of a controlled parameter, as it will be shown later on in this article. These extreme events can, for instance, take place in telecommunication data streams, where the emergence of high intensity peaks inside an optical fiber sets the limit of a high-speed optical communication link. Despite a plethora of studies dealing with extreme events driven by various mechanisms, an intensive and comprehensive investigation of the emergence of such extreme pulses in the mid-infrared field is still missing. Hence, it remains of prime importance to study them beforehand and understand their origins, in order to be able to better control them in future practical applications.13 Experimentally, means of identifying extreme events vary with the type of dynamics studied. Several criteria are thus generally applied to the data, and the strictest one, leading to the identification of the lowest number of extreme events, should be used. To detect extreme events from experimental data, the probability density function (PDF) of an appropriate attribute of the data needs to be analyzed, and crest or wave heights are commonly used.14 Once extracted from the data, heights are compared to a threshold value, above which events can be considered extreme. This threshold is generally defined as twice the significant height of the dataset, which represents the average of the higher third of heights.15 Another threshold widely used relies on the standard deviation () and the mean value () of the data, and an event is considered extreme if it exceeds .16 However, a criterion with a lower threshold ( or even ) is also commonly used without loss of generality.17,18 This threshold alone is however not sufficient to identify extreme events, and it is necessary to verify that the PDFs represent long-tailed distributions. In this study, we use both thresholds because, on the one hand, the threshold is more restrictive, so we can ensure that we observe extreme events. On the other hand, the threshold related to allows us to take into account more events so that we can display relevant statistics with enough data. The mechanism leading to the birth of such extreme events can be explained by an external crisis19 triggered by an external parameter, as we will explain hereafter. The optical source generates a chaotic output, and the related bifurcation diagram is bounded because the system is stable. After the crisis, trajectories can explore a much larger region in the phase space, and the bifurcation diagram expands. This process creates pulses whose maxima are well above those usually found in the chaotic output. However, the large pulses must seldom occur, because as long as the extreme events are very rare, the average value of the intensity of the peaks and its variance remain similar to the values before the crisis. If the large events become the norm, the threshold for extreme events will subsequently increase and all the events will be considered as regular. This means that the bifurcation diagram becomes stable again, and the output displays chaos without extreme events. Given the fact that chaos and positive Lyapunov exponents have already been encountered in quantum cascade lasers (QCLs) under external optical feedback (OF),20 it is not surprising to observe that these lasers can also be prone to extreme pulses. Despite that, it is clear that the formation of such events in QCLs can be further explained through strong numerical modeling, taking into account the peculiar characteristics of QCLs, in particular, the intersubband carriers dynamics and the transport properties.