# iam
標記為「iam」的 20 篇文章
IAM for AI Systems Assessment
Assessment of identity and access management vulnerabilities specific to AI service deployments.
Bedrock Attack Surface
Comprehensive red team methodology for Amazon Bedrock: model invocation API abuse, guardrails bypass techniques, custom model endpoint exploitation, IAM misconfigurations, knowledge base poisoning, and Bedrock Agents exploitation.
AWS IAM for AI Services
IAM exploitation patterns for AWS AI services: overprivileged roles, cross-account model access, service-linked roles, resource policies for Bedrock and SageMaker, and privilege escalation through AI-specific IAM actions.
AWS Bedrock Security Deep Dive
Advanced security assessment of AWS Bedrock covering model invocation controls, guardrails bypass testing, VPC configurations, and red team methodologies for foundation model APIs.
IAM Best Practices for Cloud AI Services
Cross-cloud IAM best practices for securing AI services on AWS, Azure, and GCP, covering least privilege, service identity management, cross-account access, and policy automation.
Cloud AI IAM Misconfigurations
Common IAM misconfigurations in cloud AI services and their exploitation for unauthorized model access.
GCP IAM for AI Services
IAM exploitation patterns for GCP AI services: service account exploitation, Workload Identity abuse, VPC Service Controls for AI, and privilege escalation through Vertex AI permissions.
Security Controls Comparison Matrix
Side-by-side comparison of AWS, Azure, and GCP AI security controls: IAM patterns, content filtering, guardrails, network isolation, logging, and threat detection across cloud providers.
AWS SageMaker Red Teaming
End-to-end walkthrough for red teaming ML models deployed on AWS SageMaker: endpoint enumeration, IAM policy analysis, model extraction testing, inference pipeline exploitation, and CloudTrail log review.
GCP Vertex AI Security Testing
End-to-end walkthrough for security testing Vertex AI deployments on Google Cloud: endpoint enumeration, IAM policy analysis, model serving exploitation, pipeline assessment, and Cloud Audit Logs review.
IAM for AI Systems 評量
評量 of identity and access management vulnerabilities specific to AI service deployments.
Bedrock 攻擊 Surface
Comprehensive red team methodology for Amazon Bedrock: model invocation API abuse, guardrails bypass techniques, custom model endpoint exploitation, IAM misconfigurations, knowledge base poisoning, and Bedrock 代理s exploitation.
AWS IAM for AI Services
IAM exploitation patterns for AWS AI services: overprivileged roles, cross-account model access, service-linked roles, resource policies for Bedrock and SageMaker, and privilege escalation through AI-specific IAM actions.
AWS Bedrock 安全 Deep Dive
進階 security assessment of AWS Bedrock covering model invocation controls, guardrails bypass testing, VPC configurations, and red team methodologies for foundation model APIs.
IAM Best Practices for Cloud AI Services
Cross-cloud IAM best practices for securing AI services on AWS, Azure, and GCP, covering least privilege, service identity management, cross-account access, and policy automation.
Cloud AI IAM Misconfigurations
Common IAM misconfigurations in cloud AI services and their exploitation for unauthorized model access.
GCP IAM for AI Services
IAM exploitation patterns for GCP AI services: service account exploitation, Workload Identity abuse, VPC Service Controls for AI, and privilege escalation through Vertex AI permissions.
安全 Controls Comparison Matrix
Side-by-side comparison of AWS, Azure, and GCP AI security controls: IAM patterns, content filtering, guardrails, network isolation, logging, and threat detection across cloud providers.
AWS SageMaker 紅隊演練
End-to-end walkthrough for red teaming ML models deployed on AWS SageMaker: endpoint enumeration, IAM policy analysis, model extraction testing, inference pipeline exploitation, and CloudTrail log review.
GCP Vertex AI 安全 Testing
End-to-end walkthrough for security testing Vertex AI deployments on Google Cloud: endpoint enumeration, IAM policy analysis, model serving exploitation, pipeline assessment, and Cloud Audit Logs review.