Title: Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch. Abstract: As the curation of data for machine learning becomes increasingly automated, dataset tampering is a mounting threat.  Backdoor attackers tamper with training data to embed a vulnerability in models that are trained on that data. This vulnerability is then activated at inference time by placing a "trigger'' into the model's input. Typical backdoor attacks insert the trigger directly into the training data, although the presence of such an attack may be visible upon inspection. In contrast, the Hidden Trigger Backdoor Attack achieves poisoning without placing a trigger into the training data at all.   However, this hidden trigger attack is ineffective at poisoning neural networks trained from scratch.  We develop a new hidden trigger attack,  Sleeper Agent, which employs gradient matching, data selection, and target model re-training during the crafting process.  Sleeper Agent is the first hidden trigger backdoor attack to be effective against neural networks trained from scratch. We demonstrate its effectiveness on ImageNet and in black-box settings.