Securing and scaling cloud-native workloads is becoming a growing challenge, especially in the Kubernetes ecosystem. According to Gartner, it’s estimated that over 95% of new digital workloads will be deployed on a cloud-native platform. This shift brings about the rise in observability and security tools that can operate with little or no performance overhead and deep kernel-level visibility.
Traditional security and observability approaches often rely on kernel modules or sidecar agents. These dependencies introduce overhead and blind spots in a dynamic and complex environment. For this reason, industries are moving towards eBPF (extended Berkeley Packet Filter). eBPF runs sandboxed programs without modifying the kernel source code, enabled by the eBPF verifier.
In this article, we’ll explore how eBPF helps secure and scale cloud-native workloads. Additionally, we will break down its core capabilities and demonstrate how it integrates with Kubernetes environments for security and observability with practical use cases.
Understanding eBPF’s Power in the Linux Kernel
eBPF is a revolutionary kernel technology that can run sandboxed programs that extend the kernel’s capabilities without modifying the kernel source code. eBPF was originally designed for packet filtering. However, it has evolved into a general-purpose execution engine used for monitoring, tracing, security, and networking without modifying the source code or loading additional kernel modules.
At its core, we have an eBPF verifier, which is a key component that inspects every program before execution. This verifier ensures safety by validating types, loops, bounds, and memory access. As a result, programs are prevented from crashing the system or degrading performance, making eBPF production-safe for large-scale environments.
Once confirmed, eBPF programs are JIT-compiled into the cloud native machines’ code before being attached to different kernel hooks, such as:
- Tracepoints and Kprobes for kernel function monitoring and system calls
- Socket filters for network packet inspection
- Cgroups and XDP (eXpress Data Path) for fine-grained process and networking control
This allows observability and scalability to take place at the system level, without depending on user-space agents. The power of eBPF lies in its capability to provide real-time and in-kernel programmability that enables developers and engineers to write dynamic instrumentation code that fits into workloads on the fly.
eBPF for Securing Cloud-Native Workloads
Securing cloud-native workloads does not just require traditional perimeter defenses such as firewalls and static policy. It’s about managing traffic between ephemeral containers, identifying runtime anomalies, and the application of least privileges at all levels of execution.
Kubernetes offers structures like Kubernetes network policies that operate in L3/L4, which may not even reflect the real-time runtime behavior of the system. Also, relying entirely on kernel modules and sidecars often introduces an overhead and can develop blind spots in high-scale distributed systems.
This is where eBPF shines. eBPF provides a granular, kernel-level visibility and control aiding real-time security enforcement across the entire system. Here are some of the ways eBPF is transforming the enforcement of security in cloud-native workloads.
1. Monitoring System Calls for Runtime Threat Detection
Most system exploits are through system calls. These exploits on the system lead to privilege escalation, container escape, and remote code execution. According to Red Hat’s 2023 State of Kubernetes Security Report, about 55% of organizations experienced a Kubernetes security incident due to misconfiguration and runtime threats.
With eBPF, system calls can be traced and monitored in real-time to detect suspicious behavior across clusters.
For example, this eBPF program is attached to the execute `syscall` to identify if a container attempts to spawn a shell (This is a common sign of container compromise).
“`
SEC(“kprobe/sys_execve”)
int kprobe__sys_execve(struct pt_regs *ctx) {
char comm[80];
bpf_get_current_comm(&comm, sizeof(comm));
// Match suspicious command
if (__builtin_memcmp(comm, “sh”, 2) == 0 || __builtin_memcmp(comm, “bash”, 4) == 0) {
bpf_trace_printk(“Alert: Shell execution detected: %s\n”, comm);
}
return 0;
}
“`
This program makes sure there is visibility into the runtime behavior of the container, even if the container is short-lived. This is what a traditional tool might not capture. This means eBPF can provide early detection of malicious command execution, and remote code execution without sidecar dependencies.
2. Enforcing Fine-Grained Network Policy Beyond L3/L4
Kubernetes network policies often provide basic pod communication. However, the functionality of these network policies does not provide insights into the dynamics of the application layer behavior and packet contents.
With eBPF, you can inspect, monitor, filter, and enforce policies on a much deeper and advanced level. For example, this eBPF program acts as a socket filter to block traffic for a forbidden IP address:
“`
SEC(“socket”)
int block_malicious_ip(struct __sk_buff *skb) {
struct iphdr *ip = (void *)(long)skb->data;
// Match a known malicious IP (e.g., 203.0.113.5)
if (ip->daddr == bpf_htonl(0xCB007105)) {
bpf_trace_printk(“Blocked outbound traffic to suspicious IP\n”);
return 0; // Drop packet
}
return 1; // Allow packet
}
“`
This packet inspection is done directly at the kernel level. It enforces traffic rules without depending on higher-level use applications. The benefit of this is that it prevents lateral movements, data exfiltrations, and command-and-control callbacks.
3. Detecting File Access Anomalies
Most containers may be tightly coupled to only allow access to specific configurations and files. However, there could be a signal compromise when there is an unauthorized attempt to access sensitive files such as `/etc/passwd` or `/var/run/secrets`.
eBPF can monitor file open events in real time:
“`
SEC(“tracepoint/syscalls/sys_enter_openat”)
int trace_open(struct trace_event_raw_sys_enter *ctx) {
char filename[256];
bpf_probe_read_user_str(&filename, sizeof(filename), (void *)ctx->args[1]);
if (__builtin_memcmp(filename, “/etc/passwd”, 12) == 0) {
bpf_trace_printk(“Suspicious file access: %s\n”, filename);
}
return 0;
}
“`
This program monitors anomalous access to critical files and containers by enforcing least privilege.
Here is the point: eBPF not only performs deep kernel-level visibility but does it perfectly without changing performance in DevOps workflows. In a cloud-native ecosystem, systems are becoming more complex, dynamic, distributed, and ephemeral, which makes security control more aligned with integration with the kernel. This is the need eBPF meets.
Scaling Observability and Network Insight with eBPF
For a cloud-native workflow, observability becomes as important as security as it scales across multiple clusters. Due to the complexity of cloud-native applications, it’s important to have a deep understanding of the system performance, behavior, and latency in real-time. According to the CNCF 2023 Annual Survey, over 70% of respondents cite a lack of kernel-level visibility as a key barrier to effective observability. Traditional agents will struggle as a result of overhead and incomplete backgrounds.
However, eBPF provides an observability section that’s built into the kernel, rendering low-cost, high visibility without intrusive instrumentation. Here are some ways eBPF scales low-overhead observability in cloud-native environments.
1. Tracing Latency Across the Stack
Modern applications are divided into microservices, which involve multiple layers including API gateway, services, databases, etc. As a result, latency at any level of these layers can be difficult to spot. However, eBPF makes it easy to trace end-to-end request latency without modifying the application source code.
For example, this simple eBPF program attached to a `syscall` can measure the time it takes to complete.
“`
struct start_time_t {
u64 ts;
};
BPF_HASH(start_times, u32, struct start_time_t);
SEC(“kprobe/__x64_sys_read”)
int trace_entry(struct pt_regs *ctx) {
u32 pid = bpf_get_current_pid_tgid();
struct start_time_t st = {.ts = bpf_ktime_get_ns()};
start_times.update(&pid, &st);
return 0;
}
SEC(“kretprobe/__x64_sys_read”)
int trace_return(struct pt_regs *ctx) {
u32 pid = bpf_get_current_pid_tgid();
struct start_time_t *st = start_times.lookup(&pid);
if (st != NULL) {
u64 delta = bpf_ktime_get_ns() – st->ts;
bpf_trace_printk(“Read latency: %llu ns\n”, delta);
start_times.delete(&pid);
}
return 0;
}
“`
This program reports how long it took for a `read()` system call to start and finish, then logs the latency in nanoseconds. In a production environment, this can be extended to trace HTTP requests, which provides a granular latency map.
2. Monitoring TCP Connection Lifecycles
When there is proper monitoring of TCP state transitions (examples of these include SYN, FIN, TIME_WAIT, etc), it makes understanding network errors a breeze. eBPF gives you the capability to efficiently monitor network errors. When it’s attached to a kernel trace-point, it can create a trigger and alert when a network connection is created, modified, or closed.
For example, here is a simplified program that logs outbound TCP connections.
“`
SEC(“tracepoint/tcp/tcp_connect”)
int trace_tcp_connect(struct trace_event_raw_tcp_event_sk *ctx) {
struct sock *sk = (struct sock *)ctx->skaddr;
u16 dport = ctx->dport;
// Convert port to host byte order
dport = ntohs(dport);
bpf_trace_printk(“Outbound TCP connection attempt on port: %d\n”, dport);
return 0;
}
“`
This program logs when there is a failure in a TCP connection attempt. This network visibility can be put together to identify activities such as port scans, service failures, or even anomalies in connection rates.
3. Real-Time Container-Level Resource Monitoring
Because of cloud-native complexity, it needs visibility into how containers use CPU, memory, and I/O in real time. eBPF can attach itself to cgroup events and schedulers to report statistics about containers without relying on intrusive agents.
For example, this eBPF program monitors process scheduling.
“`
SEC(“tracepoint/sched/sched_switch”)
int trace_sched_switch(struct trace_event_raw_sched_switch *ctx) {
u32 prev_pid = ctx->prev_pid;
u32 next_pid = ctx->next_pid;
bpf_trace_printk(“Context switch: %d -> %d\n”, prev_pid, next_pid);
return 0;
}
“`
While basic, this example demonstrates how eBPF can monitor CPU scheduling behavior across pods, offering a foundation for deeper metrics like throttling or fault tracking. When implemented in full capability, it extends its functionality to monitor metrics like CPU throttling, context switch rates, and memory faults with the use of backend monitoring tools.
Wrapping Up
With the advancement of technology and the rise of more cloud-native systems, traditional tools become obsolete in keeping up with the complexities of cloud-native systems. That’s exactly where eBPF comes into play. It gives you detailed, real-time visibility into systems and enforces security policies directly from the Linux kernel without any overhead.
In cloud-native systems like Kubernetes, eBPF helps you stay ahead in both identifying security threats and real-time, detailed observability. This includes capabilities such as detecting suspicious system calls, monitoring network traffic, and analyzing container behavior.