Cases7 cases

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1. IL-0092 Upgrading ingress-nginx from 1.12.0 to 1.14.3 brought a newer ModSecurity and OWASP Core Rule Set whose false positives blocked HMPPS Auth, so users could not sign in to their applications

   UK Ministry of Justice2026

   Symptoms

   At 08:56 on 30 March 2026, about an hour and three quarters after the rollout of a new ingress-nginx to production began, the MoJ Cloud Platform team noticed messages in the #hmpps-auth-audit-registers Slack channel about ModSecurity unexpectedly blocking requests. Users in HMPPS also raised incidents with the MoJ service desk.

   High error rateService outage

   Trigger

   The Cloud Platform team began deploying ingress-nginx 1.14.3, on images maintained by Chainguard, to the three production ingresses at 07:11 on 30 March 2026.

   Root cause

   Breaking change in an upgrade

   Component

   Ingress controllerWeb application firewall rules

   Contributing factor

   Unrepresentative staging

   Detection

   At 08:56 the Cloud Platform team saw messages in the #hmpps-auth-audit-registers Slack channel showing ModSecurity blocking requests unexpectedly; HMPPS users also raised incidents with the MoJ service desk.

   Remediation

   • rolled the ingress-nginx deployment back to the previous version, from 09:14 to 09:43
   • HMPPS applied ModSecurity rule exclusions for the rules blocking traffic to HMPPS Auth
   • added a temporary global exclusion for the CRS rule triggered by the PostHog cookie
   • redeployed ingress-nginx 1.14.3 with both exclusions in place, completed at 14:00

   Action items

   • Rescope the PostHog cookie so it is no longer sent with requests to all *.justice.gov.uk services
   • Remove the temporary global exclusion once the PostHog cookie has expired from enough users' browsers
   • Remind service teams to run dev and test workloads through the non-production ingress controllers so future ModSecurity and CRS updates are tested
   • Run production and non-production workloads in separate clusters in Cloud Platform 3.0, so non-production workloads must use the non-production ingress

   Lessons

   An ingress controller upgrade that bundles a web application firewall also changes which requests are allowed, not only how they are routed. A beta or non-production ingress only catches false positives for traffic that actually passes through it, and teams that keep their test workloads on the production ingress miss them. A fix on the client side, such as rescoping a cookie, takes effect only when old cookies have expired in users' browsers, so the rule change has to wait for it or ship with an exclusion.

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2. IL-0188 Rolling updates on EKS returned 502 errors for about 30 seconds because Kubernetes deleted old pods as soon as new pods passed readiness, before the ALB had marked the new pod targets healthy

   Yonghyun Kim2025

   Symptoms

   Every rolling update of the team's microservices on Amazon EKS, behind an Application Load Balancer, was followed by intermittent 502 and 503 errors for users. A Slack message at 3 a.m. read "배포는 성공했는데, 서비스는 죽었다" (the deployment succeeded, but the service died). Every pod was Ready and Datadog was green while users saw errors.

   High error rateService outage

   Trigger

   A rolling update of a Deployment served through an ALB that registers pod IPs as targets, with no pod readiness gates.

   Root cause

   Faulty deployment

   Component

   Cloud load balancerIngress controllerPod termination

   Contributing factor

   Unverified assumptionUnreviewed defaultsMonitoring blind spot

   Detection

   A Slack message at 3 a.m. reported that the deployment had succeeded but the service was down; Datadog dashboards were green at the time.

   Remediation

   • labelled the namespace with elbv2.k8s.aws/pod-readiness-gate-inject=enabled so the AWS Load Balancer Controller injects pod readiness gates and Kubernetes waits for the ALB target to be healthy before deleting old pods

   Lessons

   A pod being Ready in Kubernetes does not mean the load balancer will send it traffic. Without a readiness gate, a rolling update can delete the old pods while the ALB still considers the new targets unhealthy, and dashboards that track pod status stay green during the errors. Making pods wait longer on shutdown reduced the errors but did not fix the start-up gap and made every deployment much slower; the readiness gate ties the pod's Ready state to the ALB's view of the target.

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3. IL-0117 A scheduled maintenance and an ingress upgrade on OpenAI's user-facing clusters caused a networking control plane regression that briefly broke the data plane, and the services that restarted then overwhelmed a backend persistence store with a heavy first-start query

   OpenAI2024

   Symptoms

   On August 16, 2024 from 11:38 AM to 1:16 PM PT, the OpenAI primary API was unreliable: ChatGPT conversations succeeded less often, and logging in and creating accounts were affected. The status page called it elevated error rates for ChatGPT and the Platform API. The incident came in two waves, of 44 and 15 minutes.

   High error rateDatabase overload

   Trigger

   A scheduled maintenance combined with an upgrade to the ingress of OpenAI's user-facing clusters.

   Root cause

   Breaking change in an upgrade

   Component

   Ingress controllerApplication database

   Contributing factor

   No staged rolloutAutomation amplification

   Action items

   • Mitigate the networking control plane regression and validate that control plane restarts do not interfere with the clusters' data plane
   • Change service software to start faster and remove the first-start query, relieving the persistence layer
   • Deploy configuration changes that optimise the networking control plane's effect on the clusters' ability to handle traffic
   • Remove the expensive database query from critical startup paths
   • Add monitoring and alerting for networking control plane issues
   • Introduce staged rollouts with longer soak time for infrastructure changes
   • Audit systems for other slow queries that may affect service start time

   Lessons

   A brief connectivity loss turned into a long outage because recovery itself was expensive: the services that restarted overwhelmed one backend persistence store with a heavy first-start query. Keeping costly queries off startup paths shortens recovery, and staged rollouts with longer soak time limit how many systems an infrastructure regression reaches before it is caught.

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4. IL-0178 On EKS, HPA scale-in returned 502 errors because containers stopped before their pod IPs left the Service endpoints, and after blue/green switches conntrack kept sending established connections to deleted pods behind an instance-mode ALB

   Lotte E-commerce2022

   Symptoms

   LOTTE ON, Lotte E-commerce's online shopping mall of about 40 microservices on Amazon EKS, returned errors for a period of time whenever the Horizontal Pod Autoscaler (HPA) scaled pods in after load dropped. Requests reached containers that had already been deleted and failed with 502 errors.

   High error rateIntermittent timeoutsStale endpoint routing

   Trigger

   HPA scale-in deleting pods, and the deletion of Blue pods after a blue/green switch of the Service label selector.

   Root cause

   DNS or network fault

   Component

   Horizontal pod autoscalerPod terminationkube-proxyconntrack tableCloud load balancerIngress controller

   Contributing factor

   Unreviewed defaults

   Remediation

   • delayed container shutdown on pod deletion (a wait in the SIGTERM handler or a preStop sleep, capped by terminationGracePeriodSeconds) so containers stop only after the pod has left the Service endpoints
   • switched some workloads' Ingress-created ALBs from instance mode to IP mode so traffic goes straight to pod IPs without iptables and conntrack

   Action items

   • Move all workloads to ALB IP mode gradually

   Lessons

   Removing a pod from Service endpoints and stopping its containers happen at the same time in Kubernetes, so a pod can stop while traffic is still routed to it unless shutdown is delayed with a preStop hook or in the application. With an instance-mode ALB, traffic passes through node iptables and conntrack, and established connections can keep going to a pod IP after its iptables rules are gone. IP mode avoids that path, within the limit of 1000 targets per target group.

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5. IL-0115 One of OpenAI's three production AKS clusters went offline through a human error, and rebuilding it took 10½ hours because an AKS Kubernetes upgrade broke the ingress and AKS cannot be downgraded

   OpenAI2022

   Symptoms

   At 18:56 PDT on 23 June 2022 (01:56 UTC on 24 June) several models went offline at once: some fine-tuned curie and davinci models, the Codex beta models code-davinci-001 and code-cushman-001, the legacy GPT models curie, babbage and ada, and all Embeddings models.

   Service outageHigh error rateIntermittent timeouts

   Trigger

   A destructive operation on one of the three production clusters, caused by human error that the existing safeguards did not catch, took the whole cluster offline.

   Root cause

   Operator error

   Component

   Ingress controllerModel hub

   Contributing factor

   No safeguard on destructive changeNo headroomDeferred upgradesOverlapping failures

   Remediation

   • re-allocated the models from the lost cluster to the remaining two clusters
   • rebuilt the VPN link with Azure support after losing the connection to AKS during cluster setup
   • built workarounds with Azure support after losing the connection to Azure Private Link
   • tore down the cluster and rebuilt it from scratch after the upgraded Kubernetes version broke the ingress
   • moved traffic back into the rebuilt cluster between 05:30 and 06:30 PDT

   Action items

   • Add checks and locks so that this and similar production-critical infrastructure cannot be modified by accident
   • Add better visibility into the operations being performed and make parts of the system more lexicographically distinct
   • Keep working with Azure on the AKS network connectivity issues so they do not disrupt new cluster operations
   • Update playbooks with the new cluster failure modes and change the setup steps for new clusters and infrastructure
   • Upgrade Kubernetes systematically and set aside time so the clusters do not fall behind again
   • Shard fine-tuned models across multiple clusters and work towards more automatic cross-cluster replication
   • Distribute and replicate Azure Blob Storage so fine-tuned models load faster and more reliably
   • Finish rolling out node-to-node caching to relieve Azure Blob Storage bottlenecks
   • Change how model weights are stored so they are more efficient and faster to load

   Lessons

   Losing one of three clusters exhausted the GPU headroom, and GPUs could not be added on short notice, so full recovery depended on bringing the same cluster back. Rebuilding it exposed a deferred Kubernetes upgrade: the newer version broke the ingress, and because AKS cannot downgrade there was no way back except a full rebuild. Recovering many large models at once is limited by the bandwidth of the storage they load from, and the caches that normally hide this do not help when everything moves to a different cluster.

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