Backdoor attacks refer to a type of security breach in machine learning systems where an attacker intentionally manipulates the training data or model to create a hidden access point. This allows them to bypass normal authentication processes, gaining unauthorized control over the model's behavior or predictions. These attacks can undermine the integrity and reliability of ML systems, posing significant risks to privacy and security.
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