Understanding eBPF for Cloud Runtime Security & ADR

Why Kernel-Level Observability Matters in Modern CNAPP

Traditional agent-based cloud security tools rely on kernel modules or heavyweight user-space daemons that intercept system calls through ptrace or LD_PRELOAD. In dynamic Kubernetes clusters running tens of thousands of ephemeral containers per node, these legacy approaches introduce CPU overhead and stability risks.

Extended Berkeley Packet Filter (eBPF) allows sandboxed programs to execute safely inside the Linux kernel without changing kernel source code or loading unverified kernel modules.

// Example eBPF kprobe tracing execve system calls
SEC("tp/syscalls/sys_enter_execve")
int detect_exec(void *ctx) {
    char comm[16];
    bpf_get_current_comm(&comm, sizeof(comm));
    bpf_printk("Process spawned by: %s\n", comm);
    return 0;
}

Eliminating Blind Spots in Ephemeral Workloads

Snapshot-based posture scanning (CSPM) inspects disk volumes every few hours, but modern attack chains—such as in-memory reverse shells or cryptominers executed from /tmp—often complete their objective in minutes or directly within memory.

Key Telemetry Streams

• Layer 3–7 Network Flows: Correlates outbound socket connections to process PIDs and container namespaces.
• File Integrity Monitoring (FIM): Tracks file descriptor modifications on critical binaries (/etc/passwd, /root/.ssh/authorized_keys).
• Process Lineage: Maps parent-to-child process trees to distinguish authorized CI/CD executions from unauthorized lateral movement.

Architectural Tradeoffs: Agentless vs. eBPF Sensor

While agentless snapshotting provides zero-footprint visibility across multiple cloud accounts in minutes, eBPF sensors provide the runtime context required for active remediation and runtime reachability analysis.

Leading CNAPP platforms combine both: agentless discovery for cloud control plane inventory and eBPF sensors for high-criticality production workloads.