Which attack type is MOST likely to cause model drift?

AAISM defines data poisoning as directly capable of causing model drift because corrupted training data shifts the statistical distribution, leading to degraded or unsafe performance.

Model stealing (A) extracts model behavior but does not cause drift. Perfect knowledge (B) is an attacker capability, not an attack causing drift. Membership inference (D) attacks privacy, not performance.