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Early backdoor attacks against machine learning set off an arms race inattack and defence development. Defences have since appeared demonstrating someability to detect backdoors in models or even remove them. These defences workby inspecting the training data, the model, or the integrity of the trainingprocedure. In this work, we show that backdoors can be added duringcompilation, circumventing any safeguards in the data preparation and modeltraining stages. The attacker can not only insert existing weight-basedbackdoors during compilation, but also a new class of weight-independentbackdoors, such as ImpNet. These backdoors are impossible to detect during thetraining or data preparation processes, because they are not yet present. Next,we demonstrate that some backdoors, including ImpNet, can only be reliablydetected at the stage where they are inserted and removing them anywhere elsepresents a significant challenge. We conclude that ML model security requiresassurance of provenance along the entire technical pipeline, including thedata, model architecture, compiler, and hardware specification.

ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks