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Data poisoning risk for AI

Data poisoning risk for AI

Risks associated with input
Training and tuning phase
Robustness
Traditional

Description

Data poisoning is a type of adversarial attack where an adversary or malicious insider injects intentionally corrupted, false, misleading, or incorrect samples into the training or fine-tuning dataset.

Why is data poisoning a concern for foundation models?

Poisoning data can make the model sensitive to a malicious data pattern and produce the adversary’s desired output. It can create a security risk where adversaries can force model behavior for their own benefit. In addition to producing unintended and potentially malicious results, a model misalignment from data poisoning can result in business entities facing legal consequences or reputational harms.

Parent topic: AI risk atlas

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