Detections
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Detects potential AI model backdoor trigger attempts by identifying inference requests from a single caller that result in high-confidence, rare-label predictions across multiple distinct input artifacts. This pattern is indicative of an adversary probing or activating a poisoned model by injecting specific triggers designed to force anomalous outputs.
Detects adversarial backdoor triggers (such as pixel patches, watermark-like artifacts, or malicious token sequences) injected into AI inference inputs. The rule identifies these inputs by observing a combination of suspicious trigger patterns and significant, anomalous jumps in prediction confidence directed at a specific target class, indicative of a model integrity compromise.