eBPF Runtime Security for Kubernetes

Containers are ephemeral, but their processes still interact with the Linux kernel. eBPF runtime security observes that interaction close to the source, linking process execution, file access, privileges and network connections to Kubernetes workloads.

This visibility can detect behavior that image scanning cannot: a signed, vulnerability-free image can still be abused at runtime. The challenge is turning deep telemetry into stable detections without overwhelming analysts or breaking production.

What eBPF Adds

eBPF programs can attach to kernel events and filter data before sending it to user space. Kubernetes-aware tools enrich events with namespace, pod, container, labels and workload identity.

The Tetragon overview describes eBPF-based observability and enforcement for process, system-call, file and network events. Capabilities vary by kernel, tool and deployment mode.

Use cases include:

• unexpected shell or interpreter execution;
• access to sensitive files;
• privilege or capability changes;
• connections from a process that should be offline;
• execution from writable or temporary paths;
• container escape or host interaction indicators.

Start With a Runtime Threat Model

List high-value workloads, expected processes, allowed network destinations, sensitive mounts and privileges. Prioritize identity systems, CI runners, secrets services, ingress, databases and internet-facing applications.

Avoid a global baseline that treats every container alike. A build worker legitimately spawns compilers; a static web server usually should not launch a shell.

Design High-Signal Policies

Combine several dimensions:

• workload or namespace identity;
• binary path and hash;
• parent-child process relationship;
• effective capabilities and user;
• file or socket target;
• destination reputation and prevalence;
• deployment version and image digest.

Examples:

• shell launched inside a production web container;
• package manager executed after deployment;
• service account token read by an unexpected binary;
• outbound connection to a new low-reputation domain;
• process entering host namespaces or touching sensitive kernel paths.

Enrich network destinations with domain reputation and IP reputation. Runtime behavior establishes why the connection matters.

Monitoring Before Enforcement

Run new policies in observation mode. Measure volume, identify legitimate variants and attach an owner to each exception. Test rollouts against representative load and failure conditions.

Move to enforcement only when:

• the behavior is clearly forbidden;
• the policy scope is narrow;
• false-positive evidence is strong;
• rollback and break-glass paths exist;
• the team understands the enforcement action.

Killing a process may not undo an operation already performed. Enforcement semantics must match the protected action.

Pipeline and Storage

Filter at the source to avoid exporting every system call. Normalize workload identity, image digest, node and process tree. Send high-value events to the SIEM and retain raw detail long enough for investigation.

Version policies with code, peer review and tests. Connect policy changes to deployment versions so an application release does not look like attacker drift.

Incident Response

When runtime evidence indicates compromise, preserve pod specification, image digest, node, process tree, open connections, mounted secrets and relevant eBPF events. Isolate the workload according to the orchestration plan; do not rely on deleting the pod before evidence is captured.

Check the image with file hash intelligence, search related network IOCs and review Kubernetes audit logs for control-plane changes.

Metrics

Track covered workloads, high-value policies, events per workload, analyst-confirmed incidents, enforcement actions, prevented operations, false positives and exceptions without owners. Measure the time from a new threat hypothesis to a tested runtime policy.

Conclusion

eBPF gives defenders workload-aware evidence at the Linux kernel boundary. Start with a threat model, filter aggressively and enforce only narrow, well-tested invariants. Combined with container and Kubernetes security, it closes the gap between a trusted image and an untrusted runtime action.