Paper Files / Containers
Formal Architecture and Empirical Evaluation of a Defense-in-Depth Model for Kubernetes-Based Container Environments under Advanced Persistent Threats
TITLE: Formal Architecture and Empirical Evaluation of a Defense-in-Depth Model for Kubernetes-Based Container Environments under Advanced Persistent Threats ABSTRACT: The adoption of architectures based on containers orchestrated by Kubernetes has become predominant in cloud-native infrastructures due to their elasticity, portability, and operational automation capabilities. However, the increasing complexity of these environments introduces an expanded attack surface that can be exploited by sophisticated adversaries, including campaigns of Advanced Persistent Threats (APTs). In particular, vectors such as image supply chain compromise, exploitation of shared kernel vulnerabilities, abuse of control plane permissions, and lateral movement between pods represent critical challenges for security teams. In this work, we present a formal defense-in-depth architecture for Kubernetes clusters, integrating preventive control mechanisms, behavioral detection, and automated response. We model the defense system as a security state graph G=(V,E), in which vertices represent cluster security configurations and edges represent transitions induced by adversarial actions or mitigation mechanisms. The proposed architecture combines declarative security policies (Open Policy Agent/Gatekeeper and Kyverno), eBPF-based system call inspection (Falco), network segmentation with layer 7 policies (Cilium), image vulnerability analysis (Trivy), and observable telemetry integrated into the cloud-native security ecosystem. We conducted an empirical evaluation in an experimental environment containing multiple Kubernetes nodes and representative workloads of distributed applications. The experimental scenario reproduced techniques documented in the MITRE ATT&CK framework for containers, including privilege escalation, container escape, and data exfiltration. The results indicate that the defense-in-depth approach significantly reduces the observable attack surface and improves the Mean Time To Detect (MTTD). We observed that the integration of admission policies, static image analysis, and real-time eBPF-based monitoring enables the detection of malicious behavioral patterns with greater precision, reducing false positives when compared to isolated approaches. We conclude that the formalization of security policies combined with observable kernel instrumentation constitutes a promising path to strengthen the resilience of Kubernetes platforms against persistent adversaries. The work contributes an analytical model, a reference architecture, and experimental results that can guide the development of security strategies for large-scale cloud-native environments.
- Introduction
Cloud-native computing has consolidated itself as the dominant paradigm for deploying modern distributed applications. Containers and orchestrators such as Kubernetes enable infrastructure abstraction, operational elasticity, and portability across execution environments (BURNS et al., 2016). However, these same characteristics introduce new security challenges arising from the dynamic nature of clusters, intensive automation, and the coexistence of multiple components with distinct privileges.
Recent literature documents that Kubernetes environments frequently become targets of intrusion campaigns that exploit misconfigurations, exposed credentials, or vulnerabilities in container images (RAHMAN; WILLIAMS, 2020). In particular, advanced persistent threats may exploit attack chains composed of multiple stages, including initial compromise, lateral movement, and persistence within the cluster.
The complexity of the Kubernetes ecosystem makes the adoption of isolated defense mechanisms insufficient. Instead, a defense-in-depth model becomes necessary, combining multiple layers of protection, ranging from image construction to kernel-level runtime monitoring.
In this work, we propose a formal architecture for defense in depth in Kubernetes environments. Our objective is to integrate preventive, detective, and reactive mechanisms within a systematic model capable of reducing the attack surface and improving the ability to detect malicious activity.
The main contributions are:
- Formalization of a security state model for Kubernetes clusters.
- Proposal of an integrated architecture based on declarative policies, eBPF inspection, and network segmentation.
- Empirical evaluation using attack scenarios derived from the MITRE ATT&CK framework.
- Background
2.1 Containers and Kubernetes
Containers provide lightweight isolation based on Linux kernel namespaces and cgroups, enabling applications to run in encapsulated environments (MERKEL, 2014). Kubernetes acts as the control plane for orchestrating these containers, managing scheduling, networking, and workload scalability.
The Kubernetes architecture is composed of multiple components:
| Component | Function |
|---|---|
| API Server | Central control interface |
| Scheduler | Distribution of pods across nodes |
| Kubelet | Container management on each node |
| etcd | Cluster state storage |
Although this model provides great operational flexibility, it also creates multiple potential points of compromise.
2.2 Threat Model
The MITRE ATT&CK framework for containers documents techniques used by adversaries to compromise container-based environments (MITRE, 2023). Among the most relevant techniques are:
- Remote execution within compromised containers
- Privilege escalation via kernel vulnerabilities
- Lateral movement between pods
- Data exfiltration through the cluster network
Real-world cases demonstrate exploitation of shared kernel vulnerabilities, such as CVE-2022-0185, which allowed privilege escalation in certain Linux environments (NVD, 2022).
2.3 Cloud-Native Security
NIST defines guidelines for container security in SP 800-190, emphasizing the need for distributed controls throughout the entire application lifecycle (NIST, 2017). These recommendations include:
- image verification
- role-based access control
- runtime monitoring
- network isolation
- Formal Security Model
We represent the security state of a Kubernetes cluster as a directed graph:
$$ G=(V,E) $$
where $V$ represents the set of possible system states and $E$ represents transitions induced by legitimate or malicious actions.
Each state $v_i$ can be described by a vector:
$$ v_i = (c,p,n,r) $$
where:
- $c$ represents cluster configurations
- $p$ represents permissions and identities
- $n$ represents network policies
- $r$ represents the runtime state of workloads
The probability of compromise for a state can be estimated by:
$$ P(v_i) = 1 - \prod_{k=1}^{m} (1 - p_k) $$
where $p_k$ represents the probability of exploitation of a specific vulnerability present in the state.
Defense mechanisms reduce the effective attack space by eliminating malicious transition edges in the graph.
- Defense-in-Depth Architecture
The proposed architecture combines multiple security mechanisms distributed across the execution stack.
| Layer | Technology | Function |
|---|---|---|
| Build | Trivy | vulnerability analysis |
| Admission | OPA Gatekeeper | policy validation |
| Runtime | Falco (eBPF) | behavioral detection |
| Network | Cilium | segmentation and observability |
| Policy | Kyverno | declarative policies |
4.1 Image Verification
Vulnerability analysis in images reduces the likelihood of vulnerable software being executed.
Example execution with Trivy:
trivy image nginx:latest
4.2 Admission Policies
Gatekeeper makes it possible to define formal restrictions over Kubernetes objects.
apiVersion: constraints.gatekeeper.sh/v1beta1
kind: K8sRequiredLabels
metadata:
name: require-security-label
spec:
match:
kinds:
- apiGroups: [""]
kinds: ["Pod"]
4.3 Runtime Monitoring
Falco uses eBPF to observe kernel system calls.
Detection occurs when an execution pattern violates predefined rules, such as:
- execution of an interactive shell in a container
- writing to sensitive directories
- creation of privileged processes
- Experimental Methodology
We built an experimental environment composed of:
- a Kubernetes cluster with 5 nodes
- microservice-based workloads
- full telemetry instrumentation
Simulated attacks were implemented following MITRE ATT&CK techniques.
| Scenario | Technique | Objective |
|---|---|---|
| Remote execution | container escape | host access |
| Escalation | kernel exploit | root privileges |
| Lateral movement | stolen credentials | pod access |
- Results
The empirical evaluation demonstrates that the combination of controls significantly reduces operational risk.
| Metric | Without architecture | With architecture |
|---|---|---|
| MTTD | 14.2 min | 2.8 min |
| False positives | 12% | 4% |
| Estimated attack surface | 1.0 | 0.42 |
We observed that eBPF-based instrumentation was particularly effective in detecting anomalous runtime behavior.
- Discussion
The results suggest that defense in depth is fundamental for Kubernetes environments. No isolated mechanism was able to detect all evaluated attack scenarios. However, the integration of image analysis, admission policies, and runtime monitoring produced significantly broader coverage.
Another relevant aspect is the formalization of the cluster security state. The proposed model makes it possible to reason about attack paths and assess the impact of specific controls on the reduction of the vulnerable state space.
- Conclusion
We presented a formal architecture and an empirical evaluation of a defense-in-depth model for Kubernetes environments. The model integrates multiple security mechanisms distributed throughout the lifecycle of container-based applications.
The experiments indicate substantial improvements in the detection of malicious activity and in the reduction of the attack surface. The combination of declarative policies, eBPF inspection, and network segmentation constitutes a robust approach for mitigating persistent threats in cloud-native environments.
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