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AI & Machine Learning · Intermediate

Data Poisoning

An attack that corrupts training, fine-tuning or retrieval data to manipulate model behavior or implant backdoors.

Definition

Attackers insert malicious or mislabeled samples into datasets, scraped web content, fine-tuning sets or RAG corpora. Effects range from degraded accuracy to hidden triggers. Defenses include data provenance, validation, access control on data pipelines and anomaly detection.

Exam tip

Poisoning attacks training or knowledge data; evasion attacks inputs at inference time.

Don't confuse it with

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