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eBPF-based runtime detection lets security tools watch selected Linux kernel events while containers are running: process starts, file writes, system calls, and network connections. An image scan or configuration review can tell you what a container was built or deployed with. Kernel-level telemetry shows what it actually does afterward, and it can attach that behavior to container and Kubernetes context. The value depends on the node kernel, the privileges granted to the sensor, the quality of the detection rules, and whether an attacker already controls the host.
What does eBPF add to container security at runtime?
Image scanners and configuration reviews work on static facts: which packages are in an image, which volumes are mounted, which capabilities a pod requests. Those checks matter, but they cannot see a process that starts after deployment, a shell spawned inside a web container, or a sensitive file opened at 3 a.m. Runtime telemetry fills that gap by observing the workload as it executes.
Falco, one of the best-known open-source runtime tools, illustrates the model. It parses Linux system calls as they happen, evaluates the resulting event stream against a rule set, and raises an alert when a rule is violated. It also enriches each alert with container-runtime and Kubernetes metadata, so an alert names a pod and namespace rather than only a process ID. Its default rules cover patterns such as privilege-escalation indicators, namespace changes, writes to sensitive directories, unexpected outbound network connections, and unusual process spawns.
Those patterns describe what a tool may flag, not what is malicious. A package manager writing to a directory, or a monitoring agent opening a socket, can match a rule. The useful output of kernel telemetry is therefore a suspicious-behavior signal that a human or a downstream system must interpret in context.
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Three tools, three different questions
Runtime security is often discussed as a single category, but the eBPF projects most commonly compared answer different questions. Treating them as interchangeable leads to gaps in coverage.
Falco: rules and alerts over kernel events
Falco is built around a rules-and-alerting architecture. Its strength is the rule language and the breadth of default detections. Additional event sources can be added through plugins. Teams usually spend most of their effort on rule tuning: deciding which alerts matter in their environment, suppressing expected behavior such as a build job that writes to a cache directory, and deciding where alerts go.
Tetragon: observability with runtime enforcement
Tetragon uses eBPF for security observability and can also enforce policy at runtime, not merely report on it. Its events can be associated with Linux process and Kubernetes context. Because enforcement is part of the design, changes to policy carry more operational risk than changes to an alert rule: a misconfigured enforcement policy can block legitimate work.
Rank #2
Cilium and Hubble: network policy and flow visibility
Cilium applies eBPF to networking and network policy, and Hubble provides observability into flows between services. This answers a question that process-level monitoring does not: which workloads talked to which services, and whether that traffic was allowed. Network-flow visibility complements process and file monitoring; it does not replace them. A tool that sees every outbound connection still cannot tell you that a process inside a container was replaced by an attacker’s binary, and a process monitor cannot give you a complete service-to-service flow map.
How to compare runtime detection approaches
Feature lists and vendor claims make these tools look more alike than they are. The following criteria are more useful when evaluating a deployment:
- Event scope: Does the tool cover the system calls, process activity, file access, and network behavior that matter for your threat model?
- Context: Can each event be tied to a process, container, pod, namespace, or service identity? Without this, an alert is hard to act on.
- Detection and response: Does the system only emit alerts, or can it enforce policy and integrate with incident-response tooling?
- Deployment conditions: Which kernel features, Linux capabilities, host mounts, and orchestration settings does the sensor need?
- Operations: Can your team manage rule tuning, event volume, dropped events, upgrades, and the follow-up work each alert creates?
- Trust boundary: Can someone with host-level privileges disable the sensor or tamper with its kernel programs?
No independent, controlled head-to-head benchmark of these projects is established in the documentation available for this comparison, so this article does not rank them on speed, accuracy, or overhead. Choose on fit: Falco for rule-driven alerting across many event types, Tetragon when enforcement at the kernel level is required, and Cilium with Hubble when network policy and service flow visibility are the main concern. Many environments run more than one.
Rank #3
Kernel, privileges, and deployment prerequisites
Kernel-level telemetry is only as available as the kernel that runs it. Falco’s modern eBPF probe requires BPF ring-buffer support and a kernel that exposes BTF (BPF Type Format) type information. Its documentation says kernels at or above 5.8 are usually sufficient. Distributions frequently backport eBPF features into older kernel versions, however, so a version number alone does not prove support. Check the node you actually run.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThe probe also needs a specific set of Linux capabilities, and the exact set can vary with kernel support and operating conditions. The older kernel-module path requires full privileges. These are requirements documented for Falco; other eBPF tools have their own prerequisites, and you should read each project’s documentation rather than assuming they match.
Falco’s container setup documentation states that its default kernel-event configuration requires privileged access and may require driver installation, depending on the node’s kernel. That makes the sensor’s privileges a security decision in its own right, not a installation detail. A sensor that runs with broad host access is itself a high-value target.
Before deploying, run through this checklist on each node pool:
- Record the kernel version with
uname -ron every node image, including nodes from managed pools that may run different images. - Confirm that BTF is present by checking for
/sys/kernel/btf/vmlinux. A missing file means the modern probe path may not work on that node. - Confirm that the sensor’s security context is the narrowest one the chosen probe accepts, and document why any privileged setting is required.
- Check whether a driver must be installed for your kernel and whether that step is permitted by your node image policy.
- Run the sensor on a test node pool and confirm that it emits the events your rules expect before rolling it out widely.
Limits and trust boundaries
Kernel telemetry records observed behavior. It does not guarantee complete visibility, correct alerting, or a trustworthy host. Several conditions limit what it can establish:
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- Root-equivalent host access: Cilium’s threat model notes that an attacker with root-equivalent access to the host can disable eBPF and undermine the visibility and enforcement that depend on it.
- Privileged workloads: Privileged pods, host PID or host network namespaces, and access to container-runtime components can let a workload reach or alter what the sensor sees.
- Dropped or overloaded events: High event volume can cause dropped events, which means a gap in the record. Alert suppression can create the same effect by hiding behavior that matters.
- Rule quality: A rule that is too broad produces noise that teams learn to ignore; one that is too narrow misses the technique it was written for.
For these reasons, protect the node first, minimize workload privileges, and forward audit data to a central store that the workload itself cannot modify. Runtime detection then becomes one layer alongside least-privilege configuration and network controls, rather than a substitute for them.
Why the placement of this layer matters
Kernel-level detection is most useful when it fills the gap between preventive controls and incident response. Image scanning reduces what can be deployed with known weaknesses. Network policy limits which connections are possible. Runtime telemetry shows what happens when something gets through anyway, and it provides the timeline and process context that responders need. Each layer answers a different question, which is why no single one of them can stand in for the others.
Operators who adopt kernel-level telemetry should plan for the operational cost as seriously as the detection value: the rule set has to be maintained, alerts need owners, and the sensor’s own permissions and upgrade path must be reviewed like any other privileged component.
If you are starting from nothing, a reasonable first step is to confirm kernel and BTF support on your nodes, deploy one sensor in a non-production cluster, and review a week of alerts with the platform team before deciding which rules should page someone.
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