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Adding clean data can strengthen hidden AI backdoors, Anthropic-backed research finds in small-model experiments. The effect depends on contrasting data and a trigger; the study did not test production training pipelines.

Original research plot: adding clean MNIST examples increases triggered digit misclassification, while untriggered errors stay low; fixed-size training sets peak at intermediate poisoned fractions.

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Adding clean data can strengthen hidden AI backdoors, Anthropic-backed research finds in small-model experiments. The effect depends on contrasting data and a trigger; the study did not test production training pipelines. · ournews.ai