The Visibility Problem No One Likes to Admit
Over the past decade, systems have become more secure, more distributed, and more encrypted.
That’s a good thing.
But it has also made observability harder than ever.
With TLS 1.3 now standard, most production traffic is fully encrypted end-to-end. Traditional packet capture tools, network taps, and middleboxes see less and less of what actually matters. Even application logs often lack the system-level context needed to understand why something is happening.
The result?
Teams are flying blind at exactly the moment systems are becoming more complex.
Why Traditional Observability Breaks Down
Most observability stacks were built on one of three assumptions:
- You can inspect packets
- You can instrument the application
- You can proxy or intercept traffic
All three are increasingly problematic.
- Packet capture stops at Layer 3/4 and can’t see inside encrypted traffic.
- Application instrumentation requires code changes, rebuilds, and coordination across teams.
- Proxies and MITM approaches introduce performance overhead, operational risk, and often fail security reviews.
In modern environments – especially with TLS 1.3, containers, and zero-trust architectures – these approaches simply don’t scale.
The data still exists.
It’s just no longer visible from the outside.
Enter eBPF: Observability From the Inside Out
eBPF (extended Berkeley Packet Filter) takes a fundamentally different approach.
Instead of observing systems from the network, eBPF allows you to observe them from inside the Linux kernel itself.
At a high level:
eBPF lets you run small, safe programs inside the kernel that observe what the system is actually doing — in real time — without modifying the kernel or applications.
These programs are:
- Verified for safety by the kernel
- Loaded dynamically (no reboot required)
- Extremely low overhead
- Attached to precise kernel and user-space execution points
Why eBPF Changes the Game for TLS Observability
Traditional observability often looks like this:
Packets → Guess → Logs → Correlation
With eBPF, the flow becomes:
Kernel events → Structured signals → Correlated insight
Because eBPF runs where the truth already exists:
- You don’t infer – you observe
- You don’t sample blindly – you attach precisely
- You don’t guess which application sent traffic – you know
This is especially powerful in environments where:
- Multiple services share hosts
- Containers are short-lived
- Traffic is fully encryptedThe flow model is accurate and is intended to highlight the fundamental shift eBPF creates in dealing with modern, encrypted systems.
- Here is a breakdown of why the traditional flow is characterized by Guesswork and why eBPF provides Insight:
Concept Traditional Flow (Guesswork) eBPF Flow (Insight) Data Source External Observation Internal Observation Observation Point Network packets (outside the process) and Application logs (high-level context) Inside the Linux kernel and application process boundaries Encrypted Traffic Cannot see inside encrypted traffic (TLS blindness). You must infer what happened from related logs, network metadata, and packets. Attaches to the data before encryption or after decryption. You observe the cleartext data and context directly. Attribution You often have to guess which application, container, or process sent specific traffic. You get correlation to the exact Process ID (PID), container, and namespace in real-time. - In short, the document argues that traditional methods (like packet capture) stop at the encryption layer, forcing engineers to guess at the application-level truth by correlating incomplete external data. eBPF solves this by going inside the kernel and process, observing the data at its source to provide structured, correlated insight.
- Performance overhead matters
From Theory to Practice: eBPF and TLS in the Real World
This approach is not theoretical.
Using eBPF user-space probes (uprobes), it is possible to attach directly to TLS read and write paths inside an application process. This enables visibility into encrypted traffic before encryption and after decryption, without modifying the application, terminating TLS, or sharing cryptographic keys.
As a concrete example, we’ve published a small set of open-source demonstrations showing how this works with wolfSSL-based applications. These examples illustrate:
- Attaching eBPF programs to TLS read/write execution points
- Capturing structured metadata and payload boundaries
- Correlating TLS activity with process and system context
- Preserving strict security boundaries – no MITM, no TLS breakage
The goal of these examples is not to provide a turnkey product, but to demonstrate that kernel-level TLS observability is practical, safe, and achievable today.
Reference: https://github.com/wolfSSL/wolfssl-examples/tree/master/ebpf
What eBPF Makes Possible
In practice, eBPF enables capabilities that were previously impractical or unacceptable:
- Encrypted traffic observability without TLS interception
- Application-level traffic attribution (who talked to whom, and why)
- Runtime security detection based on real behavior
- Performance profiling with near-zero overhead
- Cross-layer correlation from kernel behavior to application logic
And because eBPF programs are dynamically loaded, these capabilities can be enabled or removed without downtime.
Why This Matters Now
Modern systems have crossed a threshold:
- Encryption is everywhere
- Workloads are ephemeral
- Perimeter-based visibility is gone
- The kernel is the last stable observation point
At the same time, Linux kernels have matured to safely support eBPF at scale — which is why cloud providers, security platforms, and observability vendors are investing heavily in it.
If you can’t see what’s happening inside the kernel, you’re forced to guess.
A Practical Perspective
eBPF is not a silver bullet.
It is Linux-specific, requires compatible kernel versions, and benefits from deep system-level expertise. Used incorrectly, it can introduce noise or unnecessary complexity. Used correctly, it replaces guesswork with signal.
It is not a replacement for every existing observability tool — but it excels at solving hard visibility problems that traditional approaches cannot touch.
Final Thoughts
TLS made systems safer.
eBPF makes them observable again.
Not by breaking encryption, adding proxies, or rewriting applications — but by observing reality at the source.
If you’re operating modern, encrypted, high-performance systems and struggling with visibility, eBPF is no longer experimental. It is a practical, production-ready foundation for the next generation of observability.
If you have questions about any of the above, please contact us at facts@wolfssl.com or call us at +1 425 245 8247
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