# feature-store
8 artikelengetagd met “feature-store”
Aanvalsoppervlak van Vertex AI
Red team methodology for Vertex AI: prediction endpoint abuse, custom training security gaps, feature store poisoning, model monitoring evasion, and pipeline exploitation.
Feature stores manipuleren
Geavanceerde technieken voor het aanvallen van feature stores die in ML-systemen worden gebruikt, waaronder feature-vergiftiging, schema-manipulatie, exploitatie van de serving-laag en integriteitsaanvallen tegen platforms zoals Feast, Tecton en Databricks Feature Store.
Beveiliging van de feature store
Securing feature stores used in ML pipelines against poisoning and unauthorized access.
Toegangscontrole voor de feature store
Access control strategies for feature stores: feature-level permissions, cross-team data leakage prevention, PII protection in features, service account management, and implementing least-privilege access for ML feature infrastructure.
Feature-poisoning-aanvallen
Techniques for poisoning feature store data to manipulate model behavior: direct feature value manipulation, time-travel attacks, online/offline store consistency exploitation, and targeted entity-level feature poisoning.
Beveiliging van de feature store (LLMOps-beveiliging)
Security overview of ML feature stores (Feast, Tecton, Vertex Feature Store): architecture and trust model, attack surfaces in online and offline stores, and the security implications of centralized feature management for ML systems.
Red team-walkthrough van Vertex AI
End-to-end walkthrough for red teaming Google Cloud Vertex AI: prediction endpoint testing, Model Garden security assessment, Feature Store probing, and Cloud Logging analysis.
Red team-walkthrough van Vertex AI (platform-walkthrough)
Complete red team walkthrough for Google Vertex AI: testing prediction endpoints, Model Garden assessments, Feature Store probing, and exploiting Vertex AI Agents and Extensions.