# drift
4 articlestagged with “drift”
AI Anomaly Detection
Detecting jailbreak attempts, unusual usage patterns, output drift, and embedding space anomalies in AI systems through statistical and ML-based methods.
anomaly-detectionjailbreak-detectiondriftembeddingintermediate
Runtime Monitoring & Anomaly Detection
Monitoring LLM applications in production for token usage anomalies, output pattern detection, behavioral drift, and using tools like Langfuse, Helicone, and custom logging.
monitoringanomaly-detectionobservabilitylangfuseheliconedriftlogging
Embedding Drift Attacks
Causing gradual embedding drift in vector stores through repeated small manipulations.
vectorattacksembeddingdrift
Continual Learning Drift Attacks
Exploiting continual learning and online training to gradually shift model behavior toward adversarial objectives.
trainingcontinual-learningdrift