# llmops
30 articlestagged with “llmops”
LLMOps Security Assessment
Assessment covering model deployment security, monitoring, CI/CD pipeline hardening, and operational threats.
LLMOps Security Assessment (Assessment)
Test your understanding of MLOps pipeline security, model deployment attacks, API security, monitoring gaps, model registry poisoning, and CI/CD for ML with 10 questions.
Advanced A/B Test Exploitation
Manipulating A/B testing frameworks to bias model selection toward less secure variants or introduce adversarial model candidates.
A/B Testing Security Implications
Security implications of A/B testing AI models including differential behavior exploitation.
AI Observability for Security
Using observability platforms to detect security anomalies in AI system behavior.
Blue-Green Deployment Attacks
Exploiting blue-green and canary deployment strategies to manipulate traffic routing and force deployment of compromised model versions.
Canary Deployments for AI Models
Implementing canary deployments that catch security regressions in AI model updates.
Continuous Training Security
Securing continuous and online learning systems against adversarial data injection and model drift manipulation.
Deployment Pipeline Attacks
Comprehensive analysis of attack vectors in ML deployment pipelines including build system compromise, artifact tampering, and deployment manipulation.
Endpoint Monitoring Strategies
Implementing comprehensive monitoring for model serving endpoints to detect attacks, anomalies, and drift in real-time.
Feature Flag Manipulation in AI Systems
Attacking feature flag systems to alter AI system behavior, enable hidden capabilities, or disable safety controls in production.
Feature Store Security
Securing feature stores used in ML pipelines against poisoning and unauthorized access.
LLMOps Security
Comprehensive overview of security across the LLMOps lifecycle: from data preparation and experiment tracking through model deployment and production monitoring. Attack surfaces, threat models, and defensive strategies for ML operations.
Inference Cost Attacks
Attacks that exploit inference cost dynamics to cause financial damage through adversarial input crafting and API abuse.
Kubernetes ML Operator Security
Security analysis of Kubernetes-based ML operators (KServe, Seldon, Ray) including privilege escalation, resource manipulation, and cross-tenant attacks.
ML Experiment Tracking Security
Securing experiment tracking systems like MLflow, Weights & Biases, and Neptune.
MLflow Security Assessment
Security assessment of MLflow deployments including tracking server vulnerabilities, artifact store exploitation, and model registry attacks.
Model Deployment Security
Security best practices for deploying LLMs to production environments.
Model Gateway Attacks
Exploiting model gateway and routing infrastructure to redirect requests, intercept responses, or manipulate model selection logic.
Model Gateway Security Patterns
Security patterns for centralized model gateway deployments including authentication, authorization, and auditing.
Model Monitoring Security Metrics
Key security metrics to monitor for deployed LLMs and alerting thresholds.
Model Rollback Security
Security implications of model rollback procedures including exposure windows and state consistency.
Model Serving Security Hardening
Best practices for securing model serving infrastructure including endpoint hardening, authentication, rate limiting, and output validation.
Model Telemetry Poisoning
Manipulating model telemetry and observability data to hide attacks, create false positives, or undermine monitoring effectiveness.
Model Versioning Security
Securing model version management including rollback safety and version validation.
Prompt Management Security
Securing prompt templates, system prompts, and prompt management infrastructure.
Prompt Template Versioning Security
Securing prompt template version management against unauthorized modifications and injection.
Prompt Versioning Attacks
Exploiting prompt management and versioning systems to inject adversarial system prompts into production deployments.
Rollback Attack Vectors
Exploiting model rollback mechanisms to force deployment of known-vulnerable model versions or disrupt service availability.
Shadow Model Detection
Detecting and preventing unauthorized shadow model deployments that bypass security controls and compliance requirements.