We knew Kubernetes was a different animal for security, but only now are we realizing the full scope of what is unique about its threats. The flexibility offered by automated deployments, shared infrastructure and the ability to scale beyond traditional server and network boundaries is apparent. But all this goodness comes with a wider threat landscape.

Looking at a surge in Kubernetes-related security breaches in 2023 resulting in terminations and fines, there are five root causes that are described in this article. Meanwhile, open source tools and platforms built on top of the Extended Berkeley Packet Filter (eBPF) have shown to be particularly effective in mitigating Kubernetes security threats, as we explore below.

Each of these five common errors is due in part to organizations struggling to adjust to the distributed, scalable and dynamic new world of cloud native apps. In this world of Kubernetes, complete observability, application visibility and comprehensive health and performance monitoring are no longer optional.

1. Alerts Without Context: The effectiveness of security measures in the rapidly expanding cloud native universe hinges on intelligent, context-aware systems. These systems must be capable of detecting unusual activities, deciding whether or not a security vulnerability is relevant for the specific app environment, prioritizing alerts based on their severity, and initiating automated root cause analyses and responses to mitigate threats.

The chart shows the 10 products that are most frequently mentioned in Kubernetes-related CVEs. The colors illustrate the type of vulnerability introduced by each individual product. (Source: ReveCom; Data: Mitre Corporation)

2. Configuration Mistakes: Kubernetes configurations are primarily defined using YAML files, which are human-readable data serialization standards. However, the simplicity of YAML is deceptive, as small errors can lead to significant security vulnerabilities. One common mistake is improper indentation or formatting, which can cause the configuration to be applied incorrectly or not at all. For example, a misconfigured YAML file might inadvertently expose a Kubernetes dashboard to the public internet without authentication, leading to unauthorized access. Similarly, including hard-coded sensitive credentials in a YAML file can provide attackers with easy access to the cluster if the file is exposed. Kubernetes relies on these YAML files for its orchestration, and even a small mistake can cascade into a disaster, potentially compromising the entire cluster.

The chart shows a co-occurrence network based on all 228 Kubernetes-related CVEs recorded in 2023. (Source: ReveCom. Data: Mitre)

3. Lack of Microsegmentation: The ransomware attack on the Toronto Public Library revealed the critical importance of network microsegmentation in Kubernetes environments. By limiting network access to necessary resources only, microsegmentation is pivotal in preventing the spread of attacks and safeguarding sensitive data. The library’s experience showed how the absence of microsegmentation allowed ransomware to proliferate rapidly across the network, leading to extensive data compromise. Implementing microsegmentation could have significantly limited the attack’s reach or even completely prevented it.
4. Absence of Continuous Monitoring: The need for continuous monitoring in Kubernetes environments cannot be overstated. This involves vigilant monitoring of network traffic, system logs and user behavior to detect anomalies indicative of a security breach. Without such continuous oversight, malicious activities can remain undetected for prolonged periods, allowing attackers to exploit confidential data or even hijack legitimate user credentials.
5. Insider Threats: The data breach at Forever 21 in 2023, impacting half a million customers, underscores the ever-present risk of insider threats. This breach, believed to have originated from a phishing attack, led to unauthorized access to the company’s Kubernetes environment and subsequent large-scale data theft. This incident emphasizes the necessity for robust internal security measures, including stringent access controls, multifactor authentication, comprehensive encryption and regular security awareness training.

How eBPF Addresses Critical Kubernetes Runtime Security Challenges

In the rapidly evolving world of containerized infrastructure, eBPF is a critical technology in addressing Kubernetes’ runtime security challenges. eBPF, originally designed for efficient network packet filtering, has evolved into a universal system-level monitoring and security tool. It runs sandboxed programs in an operating system kernel, allowing for high-level data collection and manipulation without compromising system stability or performance. This ability to safely and efficiently inspect system and network operations makes eBPF an essential asset in Kubernetes environments. It enhances context-aware alerting, enables real-time configuration auditing, supports effective network microsegmentation, provides a comprehensive system and network monitoring and bolsters insider threat detection. These capabilities are not just technical achievements; they represent significant business advantages in ensuring the security and integrity of Kubernetes deployments, directly impacting operational reliability, regulatory compliance and safeguarding sensitive data.

1. Enhanced Context-Aware Alerting: eBPF’s detailed visibility into system and network operations enables sophisticated, context-aware alerting mechanisms. Traditional alerting systems often generate a high volume of alerts, many of which lack relevance, leading to alert fatigue among security teams. eBPF’s context-aware alerting combats this by providing rich contextual information alerts, allowing teams to quickly discern false alarms from genuine threats. eBPF can provide this context information due to its deep integration within the Linux kernel, allowing it to monitor a wide range of system activities in real time. This includes tracking system calls, network traffic and application behavior at a granular level.
2. Real-Time Configuration Auditing: eBPF facilitates real-time monitoring of Kubernetes configurations, a capability crucial for businesses in dynamic environments where configurations change frequently. Immediate detection and alerting on misconfigurations or unauthorized changes are vital for maintaining continuous compliance with regulatory standards and internal policies. This continuous monitoring represents a significant advancement over traditional methods, reducing the risk of prolonged exposure to vulnerabilities that could lead to data breaches or compliance violations, both of which can have substantial financial and reputational repercussions.
3. Network Flow Monitoring for Microsegmentation: eBPF’s granular network flow monitoring is essential for implementing effective network microsegmentation, a key strategy in protecting sensitive business data. By enforcing strict security policies and preventing the lateral movement of attackers within the network, eBPF helps safeguard critical business assets. This is particularly important for businesses handling sensitive customer data or intellectual property, where breaches can lead to significant financial losses and damage to customer trust.
4. Comprehensive System and Network Monitoring: eBPF’s ability to track every system call and network packet within Kubernetes clusters provides a level of monitoring essential for detecting anomalies and potential threats early. This comprehensive monitoring is crucial for businesses to maintain operational integrity and service availability. Early detection of anomalies can prevent disruptions that might affect customer experience, lead to revenue loss, or damage brand reputation.
5. Observability for Insider Threat Detection: eBPF enhances the ability to detect and prevent insider threats by providing detailed observability into application and system behaviors. Insider threats are a growing concern for businesses, as they can lead to significant financial and intellectual property losses. The capability of eBPF to track system calls, network activities and changes to sensitive data helps identify unusual behaviors that might indicate insider threats, thereby protecting against potential internal breaches.

eBPF Is Critical for Keeping Kubernetes Safe

eBPF is the basis for creating a universal “security blanket” across Kubernetes clusters, and is applicable on premises, in the public cloud and at the edge. Its integration at the kernel level allows for immediate detection of monitoring gaps and seamless application of security measures to new and changing clusters. eBPF can automatically apply predefined security policies and monitoring protocols to any new cluster within the environment.

As new clusters are created, eBPF detects and seamlessly applies these security measures, ensuring that even temporary or recently deployed clusters are immediately covered under the same comprehensive security umbrella as existing ones, providing consistent and adaptive security across the entire Kubernetes landscape.

It is crucial to understand that each security software vendor can implement eBPF-based security capabilities differently, as eBPF simply provides these vendors with the ability to monitor and manipulate system calls and application activity directly at the kernel level.

Kubescape, an open source Kubernetes security platform, weaves eBPF-driven vulnerability scanning, policy enforcement, contextual alerting, configuration auditing and insider threat detection directly into developer IDEs, CI/CD pipelines and Kubernetes clusters.

The project was created by ARMO, which offers ARMO Platform, an enterprise-grade Kubernetes security platform powered by Kubescape. This comprehensive approach enables organizations to enhance their security posture from the earliest stages of development through to deployment and ongoing operation. Kubescape aims to provide DevSecOps teams and platform engineers with the security blanket they need to optimize developer productivity and security at the same time.

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