KUBERNETES Security Gaps to Address

We analyzed logs from 500+ Kubernetes deployments this year. The result? Same mistakes…just wearing a new hat. Here’s what’s actually breaking production in 2025 (and yes, you’ll relate to at least 5 of these): 1. 𝐒𝐢𝐝𝐞𝐜𝐚𝐫 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐬 𝐋𝐞𝐟𝐭 𝐔𝐧𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐞𝐝 Everyone loves sidecars…until they silently crash and nobody notices. ✓ 𝐅𝐢𝐱: Treat sidecars like first-class citizens in your observability stack. 2. 𝐈𝐬𝐭𝐢𝐨 𝐎𝐯𝐞𝐫𝐜𝐨𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐲𝐧𝐝𝐫𝐨𝐦𝐞 (𝐈𝐎𝐒) Teams added Istio for a “simple” problem and woke up inside a dependency hell. ✓ 𝐅𝐢𝐱: If you don’t really need service mesh, don’t implement one. 3. 𝐙𝐨𝐦𝐛𝐢𝐞 𝐂𝐫𝐨𝐧𝐉𝐨𝐛𝐬 Scheduled jobs finished 6 months ago…but the pods? Still running, still billing. ✓ 𝐅𝐢𝐱: Automate cleanup with TTL controllers. Manual audits won’t cut it. 4. 𝐇𝐚𝐫𝐝𝐜𝐨𝐝𝐞𝐝 𝐒𝐞𝐜𝐫𝐞𝐭𝐬 𝐢𝐧 𝐇𝐞𝐥𝐦 𝐂𝐡𝐚𝐫𝐭𝐬 It’s 2025, and teams are still committing secrets into Git. ✓ 𝐅𝐢𝐱: Use External Secrets or Vault. Helm has no business managing secrets. 5. 𝐔𝐧𝐛𝐨𝐮𝐧𝐝𝐞𝐝 𝐏𝐕𝐂𝐬 (𝐏𝐞𝐫𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐕𝐨𝐥𝐮𝐦𝐞 𝐂𝐡𝐚𝐨𝐬) Devs created PVCs with no lifecycle policies. Now your storage bill looks like a ransom note. ✓ 𝐅𝐢𝐱: Enforce Reclaim Policies and set size limits. 6. 𝐏𝐨𝐝 𝐃𝐢𝐬𝐫𝐮𝐩𝐭𝐢𝐨𝐧 𝐁𝐮𝐝𝐠𝐞𝐭? 𝐖𝐡𝐚𝐭’𝐬 𝐓𝐡𝐚𝐭? One bad rolling update and your entire service went down because nobody configured PDBs. ✓ 𝐅𝐢𝐱: Always define PodDisruptionBudgets for critical workloads. 7. 𝐂𝐏𝐔 𝐋𝐢𝐦𝐢𝐭𝐬 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐑𝐞𝐪𝐮𝐞𝐬𝐭𝐬 (𝐓𝐡𝐞 𝐓𝐡𝐫𝐨𝐭𝐭𝐥𝐞 𝐓𝐫𝐚𝐩) Containers starve at peak traffic while your cluster pretends it’s underutilized. ✓ 𝐅𝐢𝐱: Balance your Requests and Limits. Over-restricting kills performance. 8. 𝐈𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐍𝐨𝐝𝐞𝐒𝐞𝐥𝐞𝐜𝐭𝐨𝐫 & 𝐀𝐟𝐟𝐢𝐧𝐢𝐭𝐲 𝐑𝐮𝐥𝐞𝐬 Your GPU workloads ended up on CPU-only nodes. Brilliant. ✓ 𝐅𝐢𝐱: Define clear NodeSelectors and Affinity rules. Kubernetes isn’t a mind reader. 9. 𝐏𝐫𝐨𝐛𝐞𝐬 𝐌𝐢𝐬𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐞𝐝 = 𝐒𝐞𝐥𝐟-𝐈𝐧𝐟𝐥𝐢𝐜𝐭𝐞𝐝 𝐃𝐃𝐨𝐒 Readiness and liveness probes firing too frequently? You just DDoSed your own app. ✓ 𝐅𝐢𝐱: Tune those probes like your uptime depends on it (because it does). 10. 𝐀𝐮𝐭𝐨𝐬𝐜𝐚𝐥𝐞𝐫𝐬 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 Your HPA scaled to zero, and nobody knew why (until angry customers called). ✓ 𝐅𝐢𝐱: Always pair autoscaling configs with proper metrics dashboards and alerts. Kubernetes isn’t failing us. We’re failing to operationalize it correctly. Stop treating it like magic. Start treating it like critical infrastructure. What’s the worst K8s mistake you’ve seen recently? ♻️ 𝐑𝐄𝐏𝐎𝐒𝐓 𝐒𝐨 𝐎𝐭𝐡𝐞𝐫𝐬 𝐂𝐚𝐧 𝐋𝐞𝐚𝐫𝐧.

I built a Kubernetes Audit SOC dashboard in my production-style lab, because “green metrics” don’t mean you’re safe. Most Kubernetes observability stops at: CPU, memory, pods, restarts. Everything looks healthy… …but access control can be changing, secrets can be touched, and pods can be accessed interactively, with almost zero visibility. So I built an enterprise audit pipeline and turned it into a dashboard that answers the questions leadership actually cares about: Audit pipeline Grafana Alloy (K8s audit logs) → Loki → Grafana Security visibility (high-signal) - RBAC change rate (roles/bindings) - Secret write rate (create/update/patch/delete) - kubectl exec / port-forward / attach rate - 401/403 deny rate + top users / verbs - Non-2xx responses (when the API starts refusing requests) Platform context (so security is not “just logs”) - CPU / memory now (gauge) - Nodes ready / not ready (stat) - Restart offenders (table) - CPU & memory by node (bar gauge) The difference is simple: “We have logs” vs “We have visibility.” What makes this work (the part most people miss) Audit logs are high-volume and noisy. The win isn’t “collect everything”, the win is curation: - keep high-risk actions (RBAC, Secrets, exec/port-forward) - keep operational signals (deny spikes, non-2xx) - drop noise only after confirming you’re not blind Proof, not diagrams To validate the dashboard I simulated real operator actions: RBAC change → secret write → exec and port-forward …and watched them show up immediately in Loki and Grafana (red panels in the screenshot). If you’re building Kubernetes platforms: are you only monitoring metrics… or operating with security visibility? Repo + full setup doc (Helm + Alloy config + Loki + dashboard JSON) 👇 #Kubernetes #CloudNative #PlatformEngineering #DevSecOps #SRE

End-to-End Kubernetes Security Architecture for Production Environments This architecture highlights a core principle many teams overlook until an incident occurs: Kubernetes security is not a feature that can be enabled later. It is a system designed across the entire application lifecycle, from code creation to cloud infrastructure. Security starts at the source control layer. Git repositories must enforce branch protection, mandatory reviews, and secret scanning. Any vulnerability introduced here propagates through automation at scale. Fixing issues early reduces both risk and operational cost. The CI/CD pipeline acts as the first enforcement gate. Static code analysis, dependency scanning, and container image scanning validate every change. Images are built using minimal base layers, scanned continuously, and cryptographically signed before promotion. Only trusted artifacts are allowed to move forward. The container registry becomes a security boundary, not just a storage location. It stores signed images and integrates with policy engines. Admission controllers validate image signatures, vulnerability status, and compliance rules before workloads are deployed. Noncompliant images never reach the cluster. Inside the Kubernetes cluster, security focuses on isolation and access control. RBAC defines who can perform which actions. Namespaces separate workloads. Network Policies restrict pod-to-pod communication, limiting lateral movement. The control plane enforces desired state while assuming components may fail. At runtime, security becomes behavioral. Runtime detection tools monitor syscalls, process execution, and file access inside containers. Unexpected behavior is detected in real time, helping identify zero-day attacks and misconfigurations that bypass earlier controls. Observability closes the loop. Centralized logs, metrics, and audit events provide visibility for detection and response. Without observability, security incidents remain invisible until users are impacted. AWS Security Layer in Kubernetes AWS strengthens Kubernetes security through IAM roles for service accounts, VPC isolation, security groups, encrypted EBS and S3 storage, ALB ingress control, CloudTrail auditing, and native monitorin. ArchitectureThe cloud infrastructure layer provides the foundation. IAM manages identity, VPCs isolate networks, load balancers control ingress, and encrypted storage protects data at rest. Kubernetes security depends heavily on correct cloud configuration. Final Note: Kubernetes security failures rarely occur because a tool was missing. They occur because security was not designed into the architecture. Strong platforms assume compromise, limit blast radius, and provide visibility everywhere. When security becomes part of design, teams move faster, deploy confidently, and operate reliably at scale.

𝗧𝗵𝗲 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗕𝗹𝗮𝘀𝘁 𝗥𝗮𝗱𝗶𝘂𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗡𝗼𝗯𝗼𝗱𝘆 𝗪𝗮𝗻𝘁𝘀 𝘁𝗼 𝗧𝗮𝗹𝗸 𝗔𝗯𝗼𝘂𝘁 The security answer is clear. Every application deserves its own cluster. One breach stays one breach. The operational answer is equally clear. Managing a fleet of clusters is expensive, complex, and unsustainable for most teams. So most teams compromise. One shared cluster. Namespaces for separation. RBAC policies that are never quite perfect. The blast radius problem gets accepted rather than solved. This sits at the intersection of platform engineering and enterprise security architecture and it deserved a proper answer. I spent time working through this tension and building a proof of concept that tries to reconcile both sides. The answer I landed on is vCluster, Cilium, and Tetragon running together on a single host cluster. One cluster to operate. Isolated API servers per tenant. Kernel level runtime detection across all of it via eBPF. The full walkthrough including the architecture reasoning, working code, and a Makefile driven demo you can run locally is in my latest article. Medium article: https://lnkd.in/egHZrwTe GitHub repository: https://lnkd.in/eGieDD6G

Locking Down Kubernetes Namespaces with eBPF + Cilium Network Policies One of the most overlooked parts of Kubernetes security is east–west traffic — the communication that happens between pods inside the cluster. We spend a lot of time protecting the edges with firewalls, WAFs, and ingress controllers, but what happens if an attacker lands inside your cluster? By default, pods can talk to any other pod across namespaces. That means a compromised web app could start scanning or exfiltrating data from other workloads. For platform engineers building shared clusters, this is a serious risk. This week, I went through the exercise of hardening an application namespace that runs a public-facing landing page application. I used Cilium, an eBPF-powered CNI, to define precise network policies that: Why eBPF + Cilium? • Visibility: Hubble (built on eBPF) gives you real-time observability into allowed/denied flows. You can see what’s being dropped. • Performance: eBPF enforces rules at the kernel level without iptables overhead. • Granularity: CiliumNetworkPolicies allow label-based rules, namespace scoping, and port restrictions. What I Did 1. Defined ingress rules • Allowed only Prometheus (from the monitoring namespace) to scrape metrics. • Allowed only HAProxy (from the haproxy namespace) to send external traffic into www. • Allowed landing-page pods to call subscriber-api pods on port 3000. 2. Defined egress rules • Allowed DNS queries to kube-dns. • Allowed optional access to the internet (toEntities: world). • Allowed internal service calls (e.g., landing-page → subscriber-api). 3. Tested with Hubble • Verified dropped vs. forwarded flows. • Identified missing ingress/egress rules by watching real traffic. The Result • No lateral movement from the www namespace to other namespaces. • Explicitly allowed service-to-service communication only where needed. • Observable enforcement with eBPF tracing every packet decision. ⸻ Takeaway for platform engineers: Don’t just assume your cluster network is safe because you’ve secured the perimeter. Attackers move sideways once they’re in. By using eBPF and Cilium, you can implement a true zero-trust model inside Kubernetes, protecting workloads at the namespace and pod level. Have you locked down traffic inside your Kubernetes cluster yet? If not, start with your most exposed workloads and work inward. Leave a comment below telling me how you are hardening your East-West traffic inside Kubernetes. #Kubernetes #Cilium #eBPF #PlatformEngineering #K8sSecurity #DevSecOps #ZeroTrust

Wiz Research discovered critical RCE vulnerabilities (CVE-2025-1097, CVE-2025-1098, CVE-2025-24514, CVE-2025-1974) dubbed #IngressNightmare affecting Ingress NGINX Controller for Kubernetes. These unauthenticated vulnerabilities can lead to cluster takeover by exploiting the admission controller component. Technical Impact: IngressNightmare vulnerabilities allow attackers to inject arbitrary NGINX configurations via the admission controller, facilitating RCE through the `ssl_engine` directive which can load malicious shared libraries. Attack Vector: By sending specifically crafted Ingress objects to the unauthenticated admission controller endpoint, attackers can inject configuration directives that execute during the `nginx -t` validation process. Exploitation Chain: 1) Upload a malicious shared library using NGINX's client body buffer functionality 2) Exploit annotation parsing vulnerabilities to inject `ssl_engine` directive referencing the uploaded library 3) Gain pod access with elevated permissions to access secrets across all namespaces. Detection Opportunities: Monitor for unexpected HTTP requests to the admission webhook endpoint, suspicious library loads, and abnormal admission review requests. Affected Versions: All Ingress NGINX Controller versions prior to 1.11.5 and 1.12.1 are vulnerable.

Using unverified container images, over-permissioning service accounts, postponing network policy implementation, skipping regular image scans and running everything on default namespaces…. What do all these have in common ? Bad cybersecurity practices! It’s best to always do this instead; 1. Only use verified images, and scan them for vulnerabilities before deploying them in a Kubernetes cluster. 2. Assign the least amount of privilege required. Use tools like Open Policy Agent (OPA) and Kubernetes' native RBAC policies to define and enforce strict access controls. Avoid using the cluster-admin role unless absolutely necessary. 3. Network Policies should be implemented from the start to limit which pods can communicate with one another. This can prevent unauthorized access and reduce the impact of a potential breach. 4. Automate regular image scanning using tools integrated into the CI/CD pipeline to ensure that images are always up-to-date and free of known vulnerabilities before being deployed. 5. Always organize workloads into namespaces based on their function, environment (e.g., dev, staging, production), or team ownership. This helps in managing resources, applying security policies, and isolating workloads effectively. PS: If necessary, you can ask me in the comment section specific questions on why these bad practices are a problem. #cybersecurity #informationsecurity #softwareengineering

Top 10 Common Attacks on Kubernetes As someone who has spent years securing Kubernetes environments, I've encountered a variety of attack vectors that companies often overlook. Kubernetes is powerful, but without the right security measures in place, it becomes a playground for attackers. Here are the top 10 common attacks that I’ve seen in the wild—and what you need to be aware of to protect your clusters. 1. Insecure Images and Registries Unsecured container images or registries are easy targets for attackers, who can inject malicious code and compromise clusters. 2. Credential Theft Malware like Hildegard targets cloud keys, SSH keys, and Kubernetes tokens, enabling attackers to escalate privileges or install cryptominers. 3. Cryptominer Deployment Compromised systems often become cryptomining hubs, draining resources and causing financial damage. 4. Privilege Escalation Attackers use stolen credentials to move laterally, gaining access to sensitive systems and data. 5. Container Escape Vulnerabilities in container runtimes let attackers break out of containers and target the host system or cluster. 6. Fileless Attacks & Log Cleaning Attackers use fileless exploits, leaving no trace in traditional logs and clear logs to avoid detection. 7. Malicious Pods Rogue pods can hide malicious code, making them difficult to distinguish from legitimate applications. 8. Exposed API Servers An unsecured Kubernetes API server can be an entry point for brute-force attacks, token theft, and misconfigurations. 9. Zero-Day Vulnerabilities These vulnerabilities are particularly dangerous as they are unknown and unpatched. 10. Insider Threats Employees or contractors with legitimate access can misuse their privileges, posing significant internal risks. KTrust specializes in securing Kubernetes environments with continuous threat exposure management. Their platform identifies and prioritizes vulnerabilities through automated red team simulations and detailed risk assessments.

Kubernetes Security: A Pentester's Guide to Finding Low-Hanging Fruit 🚀 Ever wondered how attackers approach Kubernetes clusters? The first step is often finding the easy wins that get them an initial foothold. I've been diving into this fantastic two-part series on Kubernetes penetration testing, and Part 1 is a goldmine for both security engineers and developers. It outlines a practical methodology for uncovering common misconfigurations. Here's a breakdown of the key attack vectors and commands every security professional should know: 1. Discovery & Reconnaissance 🗺️ First, you need to understand your environment from inside a pod. Key Commands: # Check current service account cat /var/run/secrets/https://lnkd.in/dRw7VtyX # Explore the Kubernetes API curl -k https://$KUBERNETES_SERVICE_HOST:$KUBERNETES_PORT_443_TCP_PORT/api/v1/namespaces/default/pods # Check for high privileges kubectl auth can-i --list **2. RBAC Assessment & Privilege Escalation 🔑 This is where things get interesting! The article highlights how to check for dangerous RBAC configurations. Critical Checks: # Check if current service account can list secrets kubectl auth can-i list secrets # Check for wildcard permissions kubectl auth can-i '*' '*' # Look for pod creation privileges kubectl auth can-i create pods 3. Network Exposure & Service Discovery 🌐 What's exposed that shouldn't be? The article emphasizes checking network policies and service exposure. Key Techniques: # Discover services in the cluster kubectl get services --all-namespaces # Check for exposed dashboards kubectl get pods -n kube-system | grep dashboard # Scan for open ports from within pods nc -zv <service-ip> 1-10000 4. Secrets Management & Access 🔓 Secrets management is often where clusters fail spectacularly. Assessment Commands: # Check if we can read secrets kubectl get secrets --all-namespaces # Look for secrets in environment variables env | grep -i secret kubectl describe pod <pod-name> | grep -A10 -B10 "Environment" Key Takeaways for Defense 🛡️ Based on the pentester's approach, here's what you should implement: ✅ Apply Least Privilege: Don't use cluster-admin for applications ✅ Implement Network Policies: Restrict pod-to-pod communication ✅ Regular RBAC Audits: Use tools like kubeaudit or kubectl auth can-i ✅ Secure Service Accounts: Avoid mounting service account tokens when not needed ✅ Monitor Kubernetes API: Log and alert on suspicious activities