Key eBPF-Based OpenTelemetry Profiling Technologies and Security Issues to Watch in 2024

Why is eBPF-based OpenTelemetry Profiling Gaining Spotlight in Software Infrastructure Now?

"Inserting kernel code safely into a live system?" Sounds risky at first, but this is precisely why eBPF is hailed as a game-changer in cloud-native observability and security. Recently, the stack that layers profile data collected via eBPF onto the OpenTelemetry standard (OTLP) pipeline has been rapidly spreading, emerging as a core trend in Software Infrastructure.

Why eBPF Can Be ‘Inserted Live’: A Shift in Kernel Extension Models (Software Infrastructure)

Traditional kernel extensions, like kernel modules, came with the risk of “system crashes if loaded improperly.” In contrast, while eBPF runs inside the kernel, not just any code is allowed to execute.

Before an eBPF program is loaded into the kernel, a verifier performs static analysis.
If illegal memory access, potential infinite loops, or patterns jeopardizing stability are detected, the loading is rejected.
Thanks to this, it can be deployed relatively safely in production environments, like a “hot patch,” enabling kernel-level observability and security features.

This characteristic of a “safe kernel execution environment” is the starting point that elevates eBPF from a mere packet filter to a universal Software Infrastructure observability runtime.

Why Profiling Has Become More Crucial Now: The Final Puzzle of 4-Pillar Observability (Software Infrastructure)

Most organizations already have metrics, logs, and traces in place to some extent. Yet the essence of performance issues often remains elusive.

Why is CPU usage at 90%? Which function, lock, or GC is causing it?
Why does latency occur only on certain nodes—kernel, runtime, or library related?
What’s happening beyond container boundaries (host kernel, other processes, scheduler)?

The missing piece is profiles. Profiles reveal “where time is spent” at the function/stack level, directly pinpointing hot spots and bottlenecks that traces alone can miss. This is why Software Infrastructure is converging on the 4-pillars (metrics, logs, traces, profiles) of observability.

Why the eBPF + OpenTelemetry Combo is So Powerful: Solving Both ‘Collection’ and ‘Standardization’ (Software Infrastructure)

Profiling has traditionally been hard to adopt—language-specific agents, app changes, and deployment overhead are barriers. eBPF-based profiling significantly lowers this threshold.

You can collect stack traces from kernel events (e.g., perf-based sampling) without modifying applications.
In Kubernetes, it can be deployed as a DaemonSet on each node to observe the entire cluster.
Combined with OpenTelemetry, collected profiles flow into an integrated observability pipeline using the standard OTLP format.

The recent Dash0 Operator’s use of specialized OpenTelemetry Collectors like otelcol-ebpf-profiler to provide eBPF profiling pipelines exemplifies the market’s demand for “absorption into standard pipelines.”

‘Seeing Everything’ Means Needing Permissions: Why Attention and Caution Grow Together (Software Infrastructure)

eBPF-based profilers need the following operational properties to observe entire nodes:

Kernel requirements such as version 5.4+,
Access to /proc and /sys and host process observation requiring hostPID: true,
Often demanding elevated privileges like privileged mode or capabilities including CAP_BPF, CAP_PERFMON, and CAP_SYS_ADMIN.

From a Software Infrastructure perspective, eBPF is both a “visibility-maximizing technology” and a “powerful permissions-handling technology.” Cases like Atomic Arch’s demonstration of eBPF being used as a rootkit concealment tool have emerged, making eBPF both an observability instrument and a focus of security governance.

Conclusion: eBPF Profiling is Becoming the ‘Next-Gen Operational Foundation’ (Software Infrastructure)

The reasons why eBPF-based OpenTelemetry profiling is gaining attention now are clear:

It allows relatively safe kernel-level observability to be added during operation.
It identifies performance bottlenecks cluster-wide without app changes.
It standardizes data via OpenTelemetry, seamlessly integrating with existing pipelines.
It requires strong permissions, meaning it must be designed alongside security, supply chain, and policy considerations.

Ultimately, eBPF transcends being “just a technology to see more.” It is becoming the foundational layer that empowers Software Infrastructure teams to operate both performance and security at scale in increasingly complex cloud-native environments.

Inside the Technology of OpenTelemetry eBPF Profiler and Dash0 Operator from a Software Infrastructure Perspective

Ever wondered how Linux kernel 5.4 and above allows you to view all host-wide processes at a glance and detect CPU and memory bottlenecks at the kernel level? This section unpacks how the OpenTelemetry eBPF Profiler observes the entire node and how Dash0 Operator packages that data into a standard OTLP pipeline, turning it into instantly usable profiling insights.

The Core from a Software Infra Viewpoint: Sampling the Entire Node Without Modifying Applications

Traditional profiling usually requires application-side changes such as library insertion, agent injection, or runtime option settings. In contrast, eBPF-based profiling starts from a fundamentally different point.

It hooks into what the kernel already knows (scheduling, perf events, system calls, etc.),
safely collects running threads’ stacks at the kernel/user-space boundary,
and standardizes them into OpenTelemetry format (OTLP profiles), feeding them into the observability stack.

This approach is advantageous for the toughest problem in Software Infra: observing hundreds of workloads at once, each with diverse languages, frameworks, and deployment styles.

Inside the Software Infra Architecture: What `otelcol-ebpf-profiler` Does

At the heart of the setup adopted by Dash0 lies otelcol-ebpf-profiler, a profiling-specialized OpenTelemetry Collector image. This component performs two major roles simultaneously:

1) Loading eBPF programs and collecting events (kernel-side connection)

It loads eBPF programs into the kernel (using perf/sampling and related hooks),
periodically collects stack samples, and reads data accumulated in BPF maps.

2) Converting to profile data and exporting via OTLP (user-space processing)

It reconstructs sampled stacks into “profiles,”
then sends them to the backend (or the next collector pipeline) using the OTLP protocol.

The key point: this entire process is part of the Collector pipeline. Simply put, profiling data flows through observability pipelines just like traces, logs, and metrics—greatly simplifying the observability infrastructure.

Why See “All Host Processes” in Software Infra: `hostPID: true` and Privileged DaemonSets

To observe the entire cluster, you need to observe the entire node. Therefore, the eBPF profiler is usually deployed as a DaemonSet with one pod per node, requiring the following conditions:

hostPID: true
- Observes the host’s PID namespace directly rather than the container PID namespace.
- This enables visibility into the node’s full process tree, including processes running outside containers.
Access to /proc and /sys
- Necessary to read process metadata and kernel interfaces to interpret stacks, symbols, and context.
Privileged mode or kernel capabilities (CAP_SYS_ADMIN, CAP_BPF, CAP_PERFMON, etc.)
- Loading eBPF and using perf events requires strong privileges.
- Operationally, this means “modifying the kernel for observability,” making deployment approval, image trust, and policy control essential.

In summary, the power to see the entire host hinges on hostPID and privileges, which simultaneously introduce operational risks (permissions, supply chain, policies). This is why Software Infra teams treat this stack not just as a tool, but as a “platform layer.”

Capturing CPU/Memory Bottlenecks at the Kernel Level: How It Flows

When we say eBPF profiling “captures CPU hotspots well,” the data flow actually works like this:

1) Sampling Trigger Fires

Events like CPU sampling (perf-based) periodically occur.

2) Capture Call Stack of the Thread Running at That Moment

The kernel captures the “code path currently using the CPU” as a stack trace.
Unlike inserting libraries or timers, this method is less affected by diversity in the observed targets.

3) Accumulate Stack Samples and Probabilistically Reconstruct Which Function Paths Consume Time

Profiling differs from tracing by not recording every call, but by collecting many samples and revealing bottlenecks through distribution.
Visualized in flame graphs, the “widest blocks” emerge as bottleneck candidates.

4) Converted to OTLP Profiles for Backend Analysis

Feeding profiles in a standardized format acceptable to observability stacks enables slicing by release, service, or node for deep analysis.

Memory bottlenecks are approached similarly. While direct memory leak detection varies by implementation/backend, at minimum, the kernel-level view rapidly spots performance symptoms like CPU spikes from GC/allocation pressure, contention on specific code paths, and delays across kernel/user boundaries.

Where Dash0 Operator Truly Simplifies Software Infra Operations: Automatic Metadata Tagging

A profile is more than just stack data; in real-world use, you need answers to questions like:

Which Pod did this stack come from?
Which Namespace/Deployment/Node does it belong to?
Which version (release) launched it?

The Dash0 Operator pipeline enriches profiling data with Kubernetes metadata via processors like k8s_attributes, instantly turning profiles into a service-level diagnostic tool. It moves beyond “spotting a burning node” to pinpointing the workload and deployment version behind the problem.

Software Infra Checkpoints: What to Verify Before Adopting This Stack

Kernel/Permission Requirements: Can you support Linux 5.4+ and accept needed capabilities/privileged policies?
Deployment Form: Does deploying DaemonSet + hostPID: true align with your security and platform team agreements?
Observability Standardization: Can profile data flow via OTLP, sharing operational routines (collection, storage, query, permissions) with traces/logs/metrics?

Pass these three gates, and the OpenTelemetry eBPF Profiler + Dash0 Operator combo becomes a powerful foundation for observing cluster-wide performance without any code changes from the Software Infra standpoint.

Infrastructure Operation Paradigms in the AI Era from the Software Infra Perspective and the Role of eBPF

In an era where AI autonomously optimizes cloud infrastructure, it’s no longer enough to explain “what has changed”—you must be able to explain “why it behaved that way.” The challenge is that as AI-driven autoscaling, cost optimization, and self-healing intervene more intricately, system state changes occur more frequently, more subtly, and across broader domains (nodes, networks, runtimes, applications). At this point, understanding the complex operations of the infrastructure fully becomes almost impossible without eBPF.

The “Observation Gap” Created by AI-Driven Operations

Traditional observability typically infers root causes by combining application logs, APM traces, and infrastructure metrics. However, in an AI-infused environment, the “unexplainable zones” rapidly multiply:

Performance fluctuates without any code changes: Latency shifts due to scheduler batch rearrangements, node replacements, kernel parameter or cgroup adjustments, and network path changes.
Microservice boundaries blur further: Pods of the same service constantly move, versions mix, and traffic is dynamically rerouted.
Failures become shorter and more frequent: While automatic recovery “hides failures,” root issues like lock contention, runaway GC, or kernel-level resource competitions persist.

From a Software Infra standpoint, AI-driven operations demand not more automation but greater observability accuracy and standardized evidence collection.

Why eBPF Becomes the “Explanation Layer” in the AI Age

eBPF securely attaches programs to the Linux kernel (only code passing the verifier runs), enabling observation of system events. This trait makes it especially powerful in environments with volatility amplified by AI automation:

See everything without modifying applications: The eBPF profiler samples stacks from nearly every running process node-wide, revealing bottlenecks such as “which functions consume CPU,” “where locks get stuck,” and “how often GC triggers” —all without any code changes.
Cross kernel/runtime/container boundaries: While metrics represent averages and traces show request paths, the most accurate view of “the calling stack of the thread actually burning CPU” comes from profiling. Effects of AI-driven scheduling and resource policies surface at the kernel level, so eBPF—close to the kernel—provides the best explanatory power.
High information density relative to observability cost: Sampling-based profiling manages overhead better than full event capture, making it a practical option even on large clusters (sampling rates can balance cost and accuracy).

Why OpenTelemetry + eBPF Profiling Is Becoming the “Standard Operation”

In the AI era, teams, tools, and vendors must speak the same language about performance. Hence, profiling data is rapidly standardizing around OpenTelemetry (OTLP):

Collected by eBPF → Sent as OTLP → Labeled with Kubernetes metadata
For example, OpenTelemetry eBPF Profiler gathers kernel stack samples, converts them to OTLP profile format, and sends them downstream. When paired with processors like k8s_attributes, profiles automatically correlate with Pod/Namespace/Node info, instantly pinpointing “which workload version is problematic.”
Completes the 4-Pillar observability (metrics, traces, logs, profiles)
When AI automatically modifies infrastructure, just knowing “metrics worsened” isn’t enough. Profiles connect metric anomalies to code-level bottlenecks and identify the actual CPU-consuming functions within slow trace spans. This shifts incident response from guesswork to evidence-based.

Operational Reality: Strong Power of eBPF Requires Governance

There’s a critical twist. While eBPF is a powerful observability tool, it can also become a “hiding layer” exploited by attackers. Deploying eBPF agents—requiring kernel privileges—means the observability stack itself becomes a key attack surface in Software Infra.

Therefore, organizations accelerating IaC adoption with AI automation must enforce the following alongside eBPF operations:

Policy-based approval (Admission Controller) for workloads using privileged/hostPID/CAP_BPF permissions
Signature, SBOM, and provenance verification for profiler images, combined with registry allow-lists
Adversarial testing assuming an already compromised eBPF state to uncover blind spots

The core of infrastructure optimization in the AI era is not automation itself but an observability layer capable of explaining automation outcomes down to the kernel level. eBPF-based OpenTelemetry profiling delivers this layer in a standardized way and is rapidly shifting from being an “option” to a “fundamental premise” in future Software Infra operations.

The Double-Edged Sword of Software Infra Security: How eBPF Has Become Attackers’ Cutting-Edge ‘Stealth Tool’

eBPF is hailed today as the most powerful observability tool—enabling hooks into kernel events to inspect CPU sampling, system calls, and network flows “without modifying applications.” Yet, this very capability flips into a dark side: eBPF becomes the most covert weapon in modern supply chain attacks by enabling attackers to “see from the kernel and hide from the kernel.”

Why eBPF Is Optimized for ‘Stealth’ from a Software Infra Perspective

At its core, eBPF is an execution model that safely loads programs inside the Linux kernel (after verification) and attaches them to desired kernel event points. Observability tools exploit this to gain precise, system-wide visibility. But attackers use the identical mechanism inversely to reduce their footprint across the system.

Exploiting the limitations of userspace tools: Standard tools like ps, ls, and find ultimately rely on kernel-supplied information. By manipulating kernel events or system call-level results, attackers make malicious activity appear “normal” in userspace.
Abundance of critical hook points: Key observability touchpoints—file listings, process inspections, network connection checks—mostly traverse system call paths. eBPF naturally attaches at these junctions.
Collisions with Software Infra’s agent culture: In environments where EDRs, security agents, and observability agents already use eBPF, attackers hide in plain sight by blending into the expected “presence of eBPF,” enabling longer stealth periods.

Real-World Shockwaves in Software Infra: Supply Chain Attack with Optional eBPF Rootkit

The recently reported Atomic Arch supply chain attack illustrates eBPF’s double-edged nature vividly. Exploiting fragile trust in the package ecosystem, attackers distributed malware that included an optional eBPF rootkit. Crucially, they used eBPF not as a penetration tool but as a stealth tool.

Technically, the known modus operandi includes:

Hooking the getdents64() system call to manipulate directory entry (file list) returns—making files “exist but invisible in listings.”
Storing targets to hide (process IDs, file names, inodes) inside BPF maps under /sys/fs/bpf/ (e.g., hidden_pids, hidden_names, hidden_inodes).
Causing typical inspection routines (file checks, process listings) to omit malicious components, deceiving operators into seeing “normal signals” when running investigative commands.

The danger is straightforward: by tampering with observability’s foundational data sources, traditional detection relying on logs, processes, or file artifacts weakens substantially.

Why eBPF Can Disable Security Products Too (Conflict, Preemption, Disruption)

A bigger problem: countless security and observability products already depend on eBPF. If attackers load eBPF programs first, with higher privileges, then:

They can entirely hide observability targets: Events security agents must gather either never surface or appear “normal.”
They disrupt detection pipelines: Where EDRs gather events via specific syscalls or tracepoints, attackers can corrupt or circumvent that data stream, lowering signal quality.
They delay operator judgment: The worst-case Software Infra incident is “time passing with invisible root cause.” eBPF stealth excels at stretching that critical window.

Immediate Response Principles for Software Infra Operators

If you can’t abandon eBPF (and most can’t), the answer is not “no eBPF” but governance and verification.

Treat all kernel-privileged agents as ‘first-class supply chain assets’: Maintain allow-lists based on signatures, SBOMs, and provenance for eBPF/profiler/EDR images. The moment a casually installed agent gets kernel-level rights, that path becomes an attack surface.
Seal privileged deployment conditions through policies: In Kubernetes, enforce via Admission Controllers (e.g., OPA, Gatekeeper) who can use privileged/hostPID/CAP_BPF flags, in which namespaces, and with which images.
Test under ‘adversarial eBPF’ assumptions: Simulate eBPF-based stealth in test clusters to pinpoint blind spots in your monitoring/EDR/profiling. Collisions from deploying new eBPF agents onto nodes already running eBPF are especially common in real-world scenarios.

eBPF is undeniably a foundational technology elevating Software Infra observability—yet simultaneously, it offers attackers a way to “vanish at the kernel level.” Once we accept this reality, eBPF ceases to be just a performance tool; it becomes a key target of security governance.

Application and Operational Strategies for eBPF-Based Profiling That Software Infra Practitioners Must Not Miss

“Turning on” simple profiling is easy. The hard part comes afterward. AI generates IaC, automation aggressively pushes deployments, and clusters continuously evolve. To make eBPF-based OpenTelemetry profiling a sustainable operational system in this environment, there is ultimately one answer:
Implement a ‘governed eBPF observability layer’ that integrates AI governance, policies, and testing.

Below is a checklist organized in the order of “Apply → Control → Verify” with operational strategies that can be immediately applied in Software Infra practice.

Basic Deployment Model from a Software Infra Perspective: Standardize on "Privileged DaemonSet + OTLP Pipeline"

eBPF profiling typically takes the following form:

One DaemonSet per node (eBPF profiler) performs full process stack sampling
- Accesses hostPID: true, /proc, /sys
- Requires privileged mode or capabilities such as CAP_SYS_ADMIN, CAP_PERFMON, CAP_BPF
In user space, otelcol-ebpf-profiler converts stacks into OTLP profile data
OpenTelemetry pipeline (Operator/Collector) automatically attaches Pod/Namespace/Node labels via k8s_attributes
Backend analyzes via flame graphs and time-series profiles

A practical tip: make this configuration a platform-standard template as early as possible. If teams or services install separately, operational costs explode due to permission exceptions, labeling omissions, and data format mismatches.
In other words, eBPF profiling should be treated not as a tool but as a platform capability.

Software Infra Permissions and Policy Strategy: eBPF Is About ‘Controlled Permission,’ Not Just ‘Permission’

The eBPF profiler requires strong privileges to observe the kernel. A common organizational mistake is to allow too many exceptions simply because “it’s observability.” Since eBPF is a dual-use technology (both an observability and an attack tool), the following three policy points must be fixed:

1) Deployment Policy: privileged/hostPID/CAP_BPF only allowed for “specific namespaces + specific service accounts”

Separate a dedicated namespace for the profiler (e.g., observability-system), and
minimize permissions per ServiceAccount.
Enforce the following with an Admission Controller (OPA/Gatekeeper, etc.):
- Conditions for privileged Pod allowance (namespace, SA, image signature)
- Restrictions on using hostPID: true
- Allow-list capabilities such as CAP_BPF, CAP_PERFMON

2) Supply Chain Policy: eBPF images should default to “registry allow-list + signature/provenance”

Once compromised, the impact from an eBPF agent is enormous. Therefore, the Software Infra pipeline must automate at least:

Image signature verification (deploying unsigned eBPF agents is prohibited)
SBOM/provenance validation (build origin and contents checks)
Registry-level allow-list (only approved repositories/tags)

3) Runtime Policy: design inspection routines assuming “eBPF programs are already loaded”

Cases like Atomic Arch make the point clear: if attackers load eBPF first, existing observability/security can be compromised.
Thus, at least once a month, perform “hostile eBPF environment” validation.

Intentionally reproduce eBPF hook scenarios on test nodes
Verify if your profiler/EDR maintains visibility in practice
Also check for agent conflicts (hook priority, perf event resource contention)

Software Infra Operational Design: Manage “Sampling, Overhead, and Cost” as SLOs

While profiling delivers powerful insights, overhead and cost present operational risks. In practice, the right approach is not ‘maximize all the time because it looks good’ but SLO-driven operation.

Profiling sampling strategy:
- Default to low sampling for continuous operation
- Temporarily increase (burst mode) only for specific nodes/namespaces/services during faults or regressions
Overhead guardrails:
- Monitor node CPU usage, context switch increases, and Collector memory usage on dedicated dashboards
- Recommend “automatic safety mechanisms” that lower sampling or exclude nodes if thresholds are exceeded
Data retention and cardinality management:
- k8s_attributes tagging is powerful but label explosion leads to a cost explosion
- Standardize which labels to attach at the platform level and restrict ad hoc label additions

How to Combine AI Governance in Software Infra: Link “IaC Changes” and “Kernel Observability” on One Screen

In an era where AI-generated Terraform/Kubernetes manifests quickly reach production, the root cause of issues is often not “code” but change. This is where the governed eBPF observability layer shines.

Link deployment/change events (commits, release tags, Helm revisions, Terraform applies)
with profile data metadata (service/version/node/namespace)
The goal is simple:
trace back via profiles which functions, locks, or GC patterns changed due to an AI-suggested change applied at 11:10 PM yesterday
Effective operational routines include:
- On performance regression: check traces/logs for symptoms → identify culprit functions/contention via profiles → match with IaC change history
- For AI-created IaC, enforce policies/tests before deployment, then automate verification of actual impact using eBPF profiles after deployment

Software Infra Practical Checklist: 7 Questions That Determine “Successful Adoption”

Does our node kernel meet the minimum requirements (e.g., Linux 5.4+)?
Who, where, and under what conditions approves privileged/hostPID/CAP_BPF usage?
Are image signing, SBOM, and provenance enforced for eBPF agent images?
Is there a k8s_attributes tagging standard (including protection against label explosion)?
Are sampling, overhead, and cost managed by SLOs with automatic safety mechanisms?
Do we test whether our observability/security is maintained under “hostile eBPF” scenarios?
Can we correlate IaC/deployment change history with profiling data to trace root causes?

The more “yes” answers you have, the more eBPF-based profiling evolves from a one-off performance analysis tool into a sustainable operational, security, and AI governance layer within Software Infra.

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