Tag Archives: Mache learning security

ML models must also think about trusting trust

Our latest paper demonstrates how a Trojan or backdoor can be inserted into a machine-learning model by the compiler. In his Turing Award lecture, Ken Thompson explained how this could be done to an operating system, and in previous work we’d shown you you can subvert a model by manipulating the order in which training data are presented. Could these ideas be combined?

The answer is yes. The trick is for the compiler to recognise what sort of model it’s compiling – whether it’s processing images or text, for example – and then devising trigger mechanisms for such models that are sufficiently covert and general. The takeaway message is that for a machine-learning model to be trustworthy, you need to assure the provenance of the whole chain: the model itself, the software tools used to compile it, the training data, the order in which the data are batched and presented – in short, everything.