A community-beehive-registration pair must label two events: (1) someone stuffed fake “looks good” scores on generated inspection reports; (2) a generated hive-sensor script grew an unexpected remote call. How should these events be mapped?
Select an answer to reveal the explanation.
Short Explanation
Fake “looks good” scores corrupt the learning signal — data poisoning. A script that suddenly phones home is harmful generated code — malicious code generation. One pollutes ratings or training; the other plants danger in the artifact you almost ran.
Full Explanation
Data poisoning concerns manipulated training or rating data, such as fake positive scores on generated reports. Malicious code generation concerns harmful side effects inside generated code, such as unexpected remote calls. Separating the two helps testers choose review of feedback governance versus review of generated scripts.