# feature-store
標記為「feature-store」的 16 篇文章
Vertex AI Attack Surface
Red team methodology for Vertex AI: prediction endpoint abuse, custom training security gaps, feature store poisoning, model monitoring evasion, and pipeline exploitation.
Manipulating Feature Stores
Advanced techniques for attacking feature stores used in ML systems, including feature poisoning, schema manipulation, serving layer exploitation, and integrity attacks against platforms like Feast, Tecton, and Databricks Feature Store.
Feature Store Security
Securing feature stores used in ML pipelines against poisoning and unauthorized access.
Feature Store Access Control
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 Attacks
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.
Feature Store Security (Llmops Security)
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.
Vertex AI Red Team Walkthrough
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.
Vertex AI Red Team Walkthrough (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.
Vertex AI 攻擊面
為 Vertex AI 之紅隊方法論：預測端點濫用、自訂訓練安全缺口、特徵儲存投毒、模型監控逃避與管線利用。
Manipulating Feature Stores
進階 techniques for attacking feature stores used in ML systems, including feature poisoning, schema manipulation, serving layer exploitation, and integrity attacks against platforms like Feast, Tecton, and Databricks Feature Store.
Feature Store 安全
Securing feature stores used in ML pipelines against poisoning and unauthorized access.
Feature Store Access Control
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 投毒 攻擊s
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.
特徵儲存安全（LLMops 安全）
ML 特徵儲存（Feast、Tecton、Vertex Feature Store）之安全概觀：架構與信任模型、線上與離線儲存中之攻擊面，與為 ML 系統之集中化特徵管理之安全意涵。
Vertex AI 紅隊 導覽
End-to-end walkthrough for red teaming Google Cloud Vertex AI: prediction endpoint testing, 模型 Garden security assessment, Feature Store probing, and Cloud Logging analysis.
Vertex AI 紅隊 導覽 (Platform 導覽)
Complete red team walkthrough for Google Vertex AI: testing prediction endpoints, 模型 Garden assessments, Feature Store probing, and exploiting Vertex AI 代理s and Extensions.