Cases36 cases

1 to 5 of 36

1. IL-0179 With DEBUG logging and synchronous log output, a high-traffic service filled the 64 KB stdout pipe, so request threads blocked in write() while holding database connections and HikariCP ran dry, failing requests in 30-second bursts

   MUSINSA2026

   Symptoms

   The on-call engineer for a high-traffic service was alerted that requests were failing in large batches every 30 seconds.

   High error rateIntermittent timeoutsConnection pool exhaustion

   Trigger

   DEBUG level logging was on in production when traffic surged, raising log volume to about 100 times normal while the service logged synchronously.

   Root cause

   Configuration change

   Component

   Application logging libraryContainer runtimeTelemetry agentDatabase

   Contributing factor

   Unverified assumptionExcessive blast radius

   Detection

   The on-call engineer was alerted that requests were failing in large batches every 30 seconds.

   Action items

   • Replace synchronous logging with TraceAwareAsyncAppender, which captures trace_id and span_id on the calling thread and drops logs instead of blocking when the queue is full (neverBlock=true)
   • Keep the amount of log output at a sustainable level, since an asynchronous queue still fills under sustained overload

   Lessons

   Removing AsyncAppender to keep trace IDs also removed the protection that kept request threads away from stdout back-pressure. A company-wide standard applies the same side effect to dozens of services, so check what a standard rests on and what working behaviour it takes away. ThreadLocal context does not cross into an asynchronous worker, but capturing it on the calling thread before handing the event off keeps both the IDs and the asynchronous output. Under extreme load, dropping log lines is preferable to stopping request handling.

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2. IL-0471 GitHub Actions runs failed or sat queued for 9 hours after a routine deployment's pod replacement saturated capacity

   GitHub2026

   Symptoms

   From 15:05 UTC on 6 August 2026 GitHub Actions workflow runs failed to start, failed partway through or stayed queued for a long time, on both GitHub-hosted and self-hosted runners. Some requests to the Actions REST API returned errors, and some customers saw unexpected rate limiting in their workflows.

   High error rateHigh latency

   Trigger

   A routine deployment to an internal Actions service that processes events and generates Actions jobs replaced pods, and the remaining capacity became saturated.

   Root cause

   Capacity exhaustion

   Component

   CI system

   Contributing factor

   No headroomExcessive blast radiusHarmful manual action

   Remediation

   • Expanded capacity of the affected services
   • Throttled incoming webhook-triggered work
   • Increased processing capacity for the backlog of affected events
   • Deployed changes that stopped runners from repeatedly trying to acquire invalid jobs
   • Deployed a fix for self-hosted runners that were not picking up jobs and enabled it incrementally
   • Paused migrations with GitHub Enterprise Importer
   • Restored webhook-triggered Actions workflows to full throughput
   • Rolled back the mitigation that left some ARC runners offline, and recovered those runners by hand
   • Shared CLI and UI steps for customers to clear jobs that could not be retried or canceled

   Action items

   • Improve deployment and capacity safeguards for the affected services
   • Strengthen monitoring for the conditions that preceded the incident
   • Improve the resiliency and recovery of queued work and runner assignment
   • Add automatic recovery for self-hosted runners in upcoming Runner and ARC releases
   • Reduce the risk of cascading failures and speed up recovery during large Actions disruptions

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3. IL-0234 An onboarding operator deployed without its scope file installed dev applications on other IONOS Kubernetes clusters

   IONOS Cloud2026

   Symptoms

   On 8 May 2026 IONOS Cloud reported "K8s Control Planes not available": some Managed Kubernetes customers in DE/TXL, DE/FRA and DE/FRA/2 had trouble connecting to the control plane and saw degraded Kubernetes functionality. Between 15:22 and 16:12 (UTC+2) applications meant for a new internal development cluster were installed on other customer clusters, which were also modified.

   Control plane unresponsiveImage pull failures

   Trigger

   A routine internal operation onboarded a new Managed Kubernetes development cluster. The scoping configuration file for the onboarding operator failed to reach the deployment branch because of a merge conflict, and at the same time a second change removed the operator from the exclusion list, which deployed it at once.

   Root cause

   Faulty deployment

   Component

   CI systemContainer registry and its credentials

   Contributing factor

   No configuration testsExcessive blast radiusMissed in review

   Remediation

   • Corrected the onboarding operator so it needs an explicit namespace configuration before acting on any cluster
   • Reconfigured the deployment system to fail deployments when required configuration files are missing
   • Rolled back the identified changes on the affected resources
   • Ran a recovery script, validated in staging, that removed all unintended installations and updates from the affected clusters

   Action items

   • Add a mandatory CI/CD step that confirms all required configuration files reached the target branch before any deployment starts
   • Require additional peer review and change management approval for every deployment that touches production, whatever its size
   • Give teams a way to inform each affected customer in time about the status of post-incident cleanup

   Lessons

   IONOS treats the unintended change to cluster configurations as a failure of the controls that should keep internal operations from affecting customer environments.

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4. IL-0069 Attackers stole a high-privilege service account token from a production Kubernetes cluster and used it to reach a cryptocurrency exchange's financial systems

   Unnamed cryptocurrency exchange2026

   Symptoms

   Inside the cluster the attacker left these traces: a new pod appeared in the production Kubernetes cluster that was built to expose its mounted service account token; requests authenticated as a broad management service account listed secrets and touched workloads in many namespaces; and a backdoor was placed in a running production pod.

   Unauthorised access or a foreign workload

   Trigger

   A spearphishing attack gave the attacker a foothold on a developer's workstation. The attacker used the developer's active, privileged cloud session to deploy a malicious pod to the production cluster.

   Root cause

   Credential exposure

   Component

   RBAC and service accountsKubernetes API server

   Contributing factor

   Excessive blast radius

   Lessons

   Unit 42 concludes that a single over-privileged identity turned one malicious pod into cluster-wide and then cloud-wide access. Its recommendations are least-privilege RBAC with the Restricted Pod Security Standard, short-lived projected service account tokens bound to the pod's lifetime, runtime monitoring of what containers actually do, and Kubernetes audit logging so that identity-driven changes can be seen and reconstructed.

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5. IL-0464 IONOS Managed Kubernetes control planes went unreachable for minutes at a time as their shared etcd grew and fragmented

   IONOS Cloud2026

   Symptoms

   From 14 to 16 March 2026 a subset of customer control planes on IONOS Managed Kubernetes (MK8s) became unavailable again and again, each time for several minutes. While a cluster was unreachable, operations that depend on the Kubernetes API, such as deployments, scaling and health checks, did not work reliably.

   Control plane unresponsiveImage pull failures

   Root cause

   Capacity exhaustion

   Component

   etcdKubernetes API serverManaged Kubernetes control plane and provider API

   Contributing factor

   Excessive blast radius

   Remediation

   • Defragmented the affected database instances, reclaiming about 3–4 GiB per instance
   • Shortened the database compaction intervals on the affected instances
   • Deployed a fix for the affected AI Model Hub database services
   • Applied final changes to all affected clusters

   Action items

   • Expand monitoring dashboards to show control plane health per database instance
   • Move the highest-volume clusters to dedicated database backends
   • Make incidents of this kind trigger immediate paging
   • Roll the updated compaction interval out to all shared control plane database instances
   • Run automated, scheduled defragmentation jobs on all instances
   • Rebalance clusters across database instances to reduce concentration
   • Contact customers whose clusters write disproportionate data, for example to store large security scan reports outside the control plane database
   • Introduce per-tenant resource quotas and key-count limits on the shared control plane database
   • Re-architect the shared control plane for stronger isolation, with dedicated database instances and better vertical scaling
   • Evaluate full database sharding for customers with consistently high data volumes
   • Hold regular drills on database resource exhaustion and recovery

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