In complex Kubernetes systems, reliability risks are potential failure points that can lead to outages, and identifying and mitigating these risks is crucial for maintaining system stability. Common risks in Kubernetes environments include missing CPU and memory requests, lack of memory limits, and missing liveness probes, all of which can lead to resource exhaustion or failed container restarts. Other significant risks involve the absence of redundancy across availability zones, which can result in total cluster failure if an isolated zone experiences an outage. Pods can also enter problematic states such as CrashLoopBackOff or ImagePullBackOff due to application errors, resource allocation issues, or image retrieval failures. Additional issues include unschedulable pod errors, application version non-uniformity, and init container failures, which can disrupt the deployment and operation of applications. Despite their complexity, these risks can be addressed with proper detection methods, and tools like Gremlin's automated reliability platform offer solutions to identify and resolve these vulnerabilities before they impact users.