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Data poisoning attacks are a common method used by malicious actors to manipulate AI models by injecting corrupted or biased data into the training data sets. These attacks can take various forms, including mislabeling data, injecting malicious data, and manipulating existing data. The end goal is to exploit vulnerabilities in the AI model and produce biased or harmful results.